LISp-Miner Control Language Reference,
version: 27.16.03 of 11 Mar 2018

LogVerbosityLevel | How much of the execution logged into history |

TargetPlatform | How to run a task |

TaskGenerationStatus | Task status codes |

HypothesisMutualInfluenceRelationship | Types of relationship between hypothesis and mutual influence domain knowledge |

Key |
Name |
Note |

Before | 'State before' frequencies | Quantifier test applied to the state-before set data only |

After | 'State after' frequencies | Quantifier test applied to the state-after set data only |

DiffAbs | Delta of absolute frequencies values | Quantifier test applied to the 4ft-table computed by substracting of corresponding frequencies |

DiffRel | Delta of relative frequencies values | Quantifier test applied to the 4ft-table computed by substracting of corresponding relative frequencies |

ValDiff | Difference of interest-measures | Test applied to the difference of interest-measures computed separately from each frequency table |

ValDiffAbs | Absolute difference of interest-measures | Test applied to the absolute difference of interest-measures computed separately from each frequency table |

ValRatio | Ratio of interest-measures | Test applied to the ratio of interest-measures computed separately from each frequency table |

ValRatioMax | Higher of two ratios of interest-measures | Test applied to the higher of two ratios of interest-measures (after/before or before/after) |

Key |
Name |
Note |

Before | Before | State before action |

After | After | State after action |

Key |
Name |
Note |

Stable | Stable | Stable with no action |

Variable | Variable | Variable with action possible |

Key |
Name |
Note |

Sum | Sum of frequencies | Sum of frequencies from source contingency table |

Min | Min frequency | Minimal frequency from source contingency table |

Max | Max frequency | Maximal frequency from source contingency table |

PImplication | p-Implication | a/(a+b) >= p ... at least 100*p [%] of objects satisfying A satisfy also S |

AverageDifference | Average Difference Dependence | Relative difference in number of objects satisfying S among objects satisfying A to number objects saysfying S in the whole data matrix. Paramater p in <-1;inf), similar to the Lift(X->Y)= P(Y|X)/P(Y). It holds p= P(Y|X)/P(Y)-1. |

AboveAverage | Above Average Dependence | Among objects satisfying A, there are at least 100*p [%] more objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;inf). |

BelowAverage | Below Average Dependence | Among objects satisfying A, there are at least 100*p [%] less objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;1>. |

OAD | Outside Average Dependence | Among objects satisfying A, there is at least 100*p [%] more or less objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;1>. |

LowerCriticalImplication | Lower Critical Implication | - |

UpperCriticalImplication | Upper Critical Implication | - |

DoublePImplication | Double p-Implication | - |

DoubleLowerCriticalImplication | Double Lower Critical Implication | - |

DoubleUpperCriticalImplication | Double Upper Critical Implication | - |

FoundedEquivalence | p-Equivalence | - |

LowerCriticalEquivalence | Lower Critical Equivalence | - |

UpperCriticalEquivalence | Upper Critical Equivalence | - |

SimpleDeviation | Simple Deviation | - |

Fisher | Fisher quantifier | - |

ChiSq | Chi-Squared quantifier | - |

AQ | A-quantifier | - |

EQ | E-quantifier | - |

afrequency | a-frequency | a-frequency (BASE) from source contingency table |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | Relative to number of rows matching condition. Threshold value [%] is multiplied by (number of rows matching condition)/100 |

RelAll | Relative [%] to all objects | Relative to number of all rows in the data matrix. Threshold value [%] is multiplied by (number of rows in the whole matrix)/100 |

RelMax | Relative [%] to max frequency | Relative to maximal value in the current contingency table. Threshold value [%] is multiplied by (the highest frequency)/100 |

Key |
Name |
Note |

COUNT | Count(*) | Number of rows in group. |

SUM | Sum | Sum of values in group. Each category is represented by its index. |

MIN | Minimum | Minimum value in group. Each category is represented by its index. |

MAX | Maximum | Maximum value in group. Each category is represented by its index. |

AVG | Average | Average value in group. Each category is represented by its index. |

Key |
Name |
Note |

Conjunction | Conjunction | Logical operation of conjunction |

Disjunction | Disjunction | Logical operation of disjunction |

Key |
Name |
Note |

NotSet | No boolean | Not boolean category |

True | True | Category representing 'TRUE' values |

False | False | Category representing 'FALSE' values |

Key |
Name |
Note |

Sharp | Sharp | Include border value |

Round | No sharp | Don't include border value |

Key |
Name |
Note |

Enumeration | Enumeration | Enumeration of values |

Interval | Interval | Interval of values |

FuzzyInterval | Fuzzy interval | Fuzzy intervals |

Key |
Name |
Note |

Antecedent | Antecedent | Left 4ft-cedent |

Succedent | Succedent | Right 4ft-cedent |

Condition | Condition | Condition |

FirstSet | First set | First set for difference |

SecondSet | Second set | Second set for difference |

AntecedentVar | Variable antecedent | Action in antecedent |

SuccedentVar | Variable succedent | Action in succedent |

CFAttribute | Histogram attribute | Attribute used for calculation of CF-histogram |

KLAttributeRow | Row attribute | Row attribute for KL-table |

KLAttributeCol | Col attribute | Column attribut for KL-table |

ETAttribute | ETree Attribute | Attribute used in tree |

ETClass | Class Attribute | Target class attribute |

MCAttribute | Vector Attribute | Attribute used in clustering |

ConditionedBy | MI Conditioned be | Condition for a mutual influence truthfullness scope |

NotInfluencedBy | MI Not influenced by | Condition not influencing a mutual influence truthfullness |

Key |
Name |
Note |

Abs | Absolute number | Absolute frequencies. Frequencies in histogram are left as given. |

RelCondition | Relative [%] to act condition | Relative frequencies to number of rows matching condition. Frequencies are divided by number of rows matching condtion and multiplied by 100. |

RelCategory | Relative [%] for each category | Relative frequencies for each category (number of rows matching condition to number of all). Each frequency is divided by the corresponding frequency in the whole matrix and multiplied by 100. |

Key |
Name |
Note |

Abs | Absolute number | Absolute number of steps. Count value is left as given. |

RelRange | Relative [%] to act range | Relative to number of categories in the currently selected range- 1. Count value is multiplied by ('number of categories in current range minus 1')/100 |

RelAll | Relative [%] to all categories | Relative to number of all categories minus 1. Count value is multiplied by ('total number of categories minus 1')/100 |

Key |
Name |
Note |

Abs | Absolute number | Change in frequency as an absolute number. Minimal step size is left as given. |

RelFrequencyPrev | Relative [%] to previous frequency | Relative to the previous frequency. Minimal step size is multiplied by (frequency of the previous category)/100 |

RelAll | Relative [%] to all objects | Relative to number of all rows in the data matrix. Minimal step size is multiplied by (number of rows in the whole matrix)/100 |

RelCondition | Relative [%] to act condition | Relative to number of rows matching condition. Minimal step size is multiplied by (number of rows matching condition)/100 |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal frequency in the currently processed histogram. Minimal step size is multiplied by (the highest frequency in the whole histogram)/100 |

Key |
Name |
Note |

Sum | Sum of frequencies | Sum of frequencies from range of categories |

Min | Min frequency | Minimal frequency from range of categories |

Max | Max frequency | Maximal frequency from range of categories |

Avg | Average frequency | Average frequency from range of categories |

Some | Some frequency | At least one frequency from range of categories |

VariationRatio | Variation ratio | Variation ratio = 1-f(modal) |

NominalVariation | Nominal variation (norm) | Nominal variation (norm) = suma(f_i*(1-f_i)) * K/(K-1), where f_i is frequency of the i-th category and K is number of categories |

DiscreteOrdinaryVariation | Discrete ordinary variation (norm) | Discrete Ordinary Variation (norm) = 2*suma(F_i*(1-F_i))* 2/(K-1), where F_i is cumulative relative frequency of the i-th category and K is number of categories |

MedianIA | Median-category index (absolute) | Index (absolute <1;K>) of the median category |

MedianIR | Median-category index (relative) | Index (relative <0;1>) of the median category |

ArithmeticAverage | Arithmetic average | Arithmetic average of cardinal values. Only for cardinal attributes. |

GeometricAverge | Geometric average | Geometric average of cardinal values. Only for cardinal attributes. |

Variance | Variance | Variance of cardinal values. Only for cardinal attributes. |

StDev | Standard deviation | Standard deviation of cardinal values. Only for cardinal attributes. |

Skewness | Skewness | Skewness of distribution of cardinal values. Only for cardinal attributes. |

Asymetry | Asymetry | Asymetry coeficient of distribution of cardinal values. Only for cardinal attributes. |

StepsUp | Steps-up | Number of steps-up in frequency of adjectant categories from given range in histogram |

StepsDown | Steps-down | Number of steps-down in frequency of adjectant categories from given range in histogram |

PattDiffSum | PattDiffSum | Sum of absolute values of differences of frequencies in the histogram and a given pattern |

PattDiffMin | PattDiffMin | The minimal of absolute values of differences of frequencies in the histogram and a given pattern |

PattDiffMax | PattDiffMax | The maximal of absolute values of differences of frequencies in the histogram and a given pattern |

Var | Variation from pattern | Total variation from given pattern |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | Relative to number of rows matching condition. Threshold value [%] is multiplied by (number of rows matching condition)/100 |

RelAll | Relative [%] to all objects | Relative to number of all rows in the data matrix. Threshold value [%] is multiplied by (number of rows in the whole matrix)/100 |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal frequency in the whole histogram. Threshold value [%] is multiplied by (the highest frequency in the whole histogram)/100 |

Key |
Name |
Note |

Subset | Subsets | All the subsets of categories from minimal up to maximal length |

OneCategory | One category | Only one selected category appears in literal |

Sequence | Sequences | All the consecutive sequencies of categories from minimal up to maximal length |

CyclicalSequence | Cyclical sequences | Same as the sequence plus overlapping combinations of first and last categories |

Cuts | Cuts | All the cuts of categories from minimal up to maximal length |

LeftCut | Left cuts | All the left cuts of categories from minimal up to maximal length |

RightCut | Right cuts | All the right cuts of categories from minimal up to maximal length |

Key |
Name |
Note |

Equal | Equal | Equal value |

Less | Less than | Less than |

LessOrEqual | Less than or equal | Less than or equal |

Greater | Greater than | Greater than |

GreaterOrEqual | Greater than or equal | Greater than or equal |

NotEqual | Not equal | Not equal value |

Key |
Name |
Note |

Ordinary | Ordinary DB field | Ordinary database column |

Derived | Derived | Derived column by expression |

SQLQuery | SQL-query | Column computed as an SQL query |

Geo | Geo | Column computed as a geographical relation of a point to a set of points or polygons |

DatePart | Date-time partial value | A single partial value derived from the DateTime column |

MCField | Multi-column field | One field of a multi-column |

Hypothesis | Task-results derived value | Derived column based on results of a hypothesis |

PCA | Task-results derived value | One component of a Principal component analysis |

Key |
Name |
Note |

Nominal | Nominal | Nominal values |

Ordinal | Ordinal | Ordinal values with an order defined |

Cardinal | Cardinal | Cardinal values with a distance defined |

NotSet | Not set | Unknown character of values |

Key |
Name |
Note |

Table | Stored table | Data statically stored in database table |

View | Dynamic view | Dynamically created view from (multiple) database tables |

Key |
Name |
Note |

Year | Year | Year-value of the date |

Month | Month | Month-value of the date |

Day | Day | Day-value from of the date |

Hour | Hour | Hour-value of the time |

Min | Min | Minutes-value of the time |

Sec | Sec | Seconds-value of the time |

DayOfWeek | DayOfWeek | Day of the week of the date |

DayOfYear | DayOfYear | Day of the year of the date |

WeekOfYear | WeekOfYear | Week of the year of the date |

Quarter | Quarter | Quarter of the year of the date |

DayOfRange | DayOfRange | Day index within the min-max range of dates in the corresponding column |

MonthOfRange | MonthOfRange | Month index within the min-max range of dates in the corresponding column |

NotSet | Not set | Not applicable to this attribute |

Key |
Name |
Note |

Abs | Absolute number | Absolute number of steps. Count value is left as given. |

RelRange | Relative [%] to act range | Relative to number of categories in the currently selected range- 1. Count value is multiplied by ('number of categories in current range minus 1')/100 |

RelAll | Relative [%] to all categories | Relative to number of all categories minus 1. Count value is multiplied by ('total number of categories minus 1')/100 |

Key |
Name |
Note |

Abs | Absolute number | Change in frequency as an absolute number. Minimal step size is left as given. |

RelFrequencyPrev | Relative [%] to previous frequency | Relative to the previous frequency. Minimal step size is multiplied by (frequency of the previous category)/100 |

RelAll | Relative [%] to all objects | Relative to number of all rows in the data matrix. Minimal step size is multiplied by (number of rows in the whole matrix)/100 |

RelCondition | Relative [%] to act condition | Relative to number of rows matching condition. Minimal step size is multiplied by (number of rows matching condition)/100 |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal frequency in the currently processed histogram. Minimal step size is multiplied by (the highest frequency in the whole histogram)/100 |

Key |
Name |
Note |

Sum | Sum of frequencies | Sum of frequencies from range of categories |

Min | Min frequency | Minimal frequency from range of categories |

Max | Max frequency | Maximal frequency from range of categories |

Avg | Average frequency | Average frequency from range of categories |

Some | Some frequency | At least one frequency from range categories |

VariationRatio | Variation ratio | Variation ratio = 1-f(modal) |

NominalVariation | Nominal variation (norm) | Nominal variation (norm) = suma(f(i)*(1-f(i))) * K/(K-1) |

DiscreteOrdinaryVariation | Discrete ordinary variation (norm) | Discrete Ordinary Variation (norm) = 2*suma(F(i)*(1-F(i))* 2/(K-1) |

ArithmeticAverage | Arithmetic average | Arithmetic average of cardinal values |

GeometricAverage | Geometric average | Geometric average of cardinal values |

Variance | Variance | Variance of cardinal values |

StandardDeviation | Standard deviation | Standard deviation of cardinal values |

Skewness | Skewness | Skewness of distribution of cardinal values |

Asymetry | Asymetry | Asymetry coeficient of distribution of cardinal values |

StepsUp | Steps-up | Number of steps-up in frequency of adjectant categories in histogram |

StepsDown | Steps-down | Number of steps-down in frequency of adjectant categories in histogram |

Var | Variation from pattern | Total variation from given pattern |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | Relative to number of rows matching condition. Threshold value [%] is multiplied by (number of rows matching condition)/100 |

RelAll | Relative [%] to all objects | Relative to number of all rows in the data matrix. Threshold value [%] is multiplied by (number of rows in the whole matrix)/100 |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal value in the current contingency table. Threshold value [%] is multiplied by (the highest frequency in the whole histogram)/100 |

Key |
Name |
Note |

Sum | Sum of frequencies | Sum of frequencies from source contingency table |

Min | Min frequency | Minimal frequency from source contingency table |

Max | Max frequency | Maximal frequency from source contingency table |

PImplication | p-Implication | a/(a+b) >= p ... at least 100*p [%] of objects satisfying A satisfy also S |

AverageDifference | Average Difference Dependence | Relative difference in number of objects satisfying S among objects satisfying A to number objects saysfying S in the whole data matrix. Paramater p in <-1;inf), similar to the Lift(X->Y)= P(Y|X)/P(Y). It holds p= P(Y|X)/P(Y)-1. |

AboveAverage | Above Average Dependence | Among objects satisfying A there are at least 100*p [%] more objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;inf). |

BelowAverage | Below Average Dependence | Among objects satisfying A there are at least 100*p [%] less objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;1>. |

OAD | Outside Average Dependence | Among objects satisfying A, there is at least 100*p [%] more or less objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;1>. |

LowerCriticalImplication | Lower Critical Implication | The binomical test rejects on the level alpha the null hypothesis P(S|A)<=p in favour of alternative P(S|A)>p |

UpperCriticalImplication | Upper Critical Implication | The binomical test does not reject on the level alpha the null hypothesis P(S|A)<=p in favour of alternative P(S|A)>p |

DoublePImplication | Double p-Implication | a/(a+b+c) >= p ... at least 100*p [%] of objects satisfying A or S satisfy both A and S |

DoubleLowerCriticalImplication | Double Lower Critical Implication | The binomical test rejects on the level alpha the null hypothesis P(AandS|AorS)<=p in favour of alternative P(AandS|AorS)>p |

DoubleUpperCriticalImplication | Double Upper Critical Implication | The binomical test does not reject on the level alpha the null hypothesis P(AandS|AorS)<=p in favour of alternative P(AandS|AorS)>p |

PEquivalence | p-Equivalence | (a+d)/n >= p ... at least 100*p [%] objects have the same truth value for A and S |

LowerCriticalEquivalence | Lower Critical Equivalence | The binomical test rejects on the level alpha the null hypothesis P(A and S have the same truth value)<=p in favour of alternative P(A and S have the same truth value)>p |

UpperCriticalEquivalence | Upper Critical Equivalence | The binomical test does not reject on the level alpha the null hypothesis P(A and S have the same truth value)<=p in favour of alternative P(A and S have the same truth value)>p |

SimpleDeviation | Simple Deviation | a*d > exp(sigma)*b*c |

Fisher | Fisher quantifier | The one-sided Fisher test rejects on the level alpha the null hypothesis of independence of A and S in favour of the alternative of their positive logarithmic interaction |

ChiSq | Chi-Squared quantifier | The one-sided Fisher test asymptotically rejects on the level alpha the null hypothesis of independence of A and S in favour of the alternative of their positive logarithmic interaction |

AQ | A-quantifier | - |

EQ | E-quantifier | - |

afrequency | a-frequency | a-frequency (BASE) from source contingency table |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | |

RelAll | Relative [%] to all objects | |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal value in the current contingency table. Threshold value [%] is multiplied by (the highest frequency)/100 |

Key |
Name |
Note |

Sum | Sum of frequencies | Sum of frequencies from given part of source contingency table |

Min | Min frequency | Minimal frequency from given part of source contingency table |

Max | Max frequency | Maximal frequency from given part of source contingency table |

Avg | Average frequency | Average frequency from given part of source contingency table |

Some | Some frequency | At least one frequency from given part of source contingency table |

CramerV | Cramer's V coefficient | Cramer's V (association of two nominal variables) in <0;1> (the farther is value from 0 the more dependant) |

Kendall | Kendall's TauB coefficient | Value of TauB in <-1;1> (the farther is value from 0 the more dependant) |

ChiSq | Chi-square test | Chi-square test of similarity (the greater value the more dependant) |

ConditionalEntropy | Conditional entropy H(C|R) | Conditional entropy of columns given rows (the lower value the more dependant <0;log2(L)>) |

MutualInformation | Mutual information MI(R,C) normalized | Mutual information between rows and columns (the greater value the more dependant <0;1>) |

InformationDependence | Inf. dependence ID(R,C) | Information dependence between rows and columns (the greater value the more dependant <0;1>) |

AsymetricInformation | Asymetric information coefficient AIC(R,C) | Value of asymetric information coefficient Theta (the greater value the more dependant <0;1>) |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | |

RelAll | Relative [%] to all objects | |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal frequency in the current contingency table. Threshold value [%] is multiplied by (the highest frequency in the whole KL-table)/100 |

Key |
Name |
Note |

Support | Support | a/(a+b+c+d) >= p ... at least 100*p [%] of objects satisfy both A and S |

PImplication | p-Implication | a/(a+b) >= p ... at least 100*p [%] of objects satisfying A satisfy also S |

AboveAverage | Above Average Dependence | Among objects satisfying A, there is at least 100*p [%] more objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;inf). |

BelowAverage | Below Average Dependence | Among objects satisfying A, there is at least 100*p [%] less objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;1>. |

OAD | Outside Average Dependence | Among objects satisfying A, there is at least 100*p [%] more or less objects satisfying S than there are objects satisfying S in the whole data matrix. Parameter p in <0;1>. |

AverageDifference | Average Difference Dependence | Relative difference in number of objects satisfying S among objects satisfying A to number objects saysfying S in the whole data matrix. Paramater p in <-1;inf), similar to the Lift(X->Y)= P(Y|X)/P(Y). It holds p= P(Y|X)/P(Y)-1. |

LowerCriticalImplication | Lower Critical Implication | The binomical test rejects on the level alpha the null hypothesis P(S|A)<=p in favour of alternative P(S|A)>p |

UpperCriticalImplication | Upper Critical Implication | The binomical test does not reject on the level alpha the null hypothesis P(S|A)<=p in favour of alternative P(S|A)>p |

DoublePImplication | Double p-Implication | a/(a+b+c) >= p ... at least 100*p [%] of objects satisfying A or S satisfy both A and S |

DoubleLowerCriticalImplication | Double Lower Critical Implication | The binomical test rejects on the level alpha the null hypothesis P(AandS|AorS)<=p in favour of alternative P(AandS|AorS)>p |

DoubleUpperCriticalImplication | Double Upper Critical Implication | The binomical test does not reject on the level alpha the null hypothesis P(AandS|AorS)<=p in favour of alternative P(AandS|AorS)>p |

PEquivalence | p-Equivalence | (a+d)/n >= p ... at least 100*p [%] objects have the same truth value for A and S |

LowerCriticalEquivalence | Lower Critical Equivalence | The binomical test rejects on the level alpha the null hypothesis P(A and S have the same truth value)<=p in favour of alternative P(A and S have the same truth value)>p |

UpperCriticalEquivalence | Upper Critical Equivalence | The binomical test does not reject on the level alpha the null hypothesis P(A and S have the same truth value)<=p in favour of alternative P(A and S have the same truth value)>p |

SimpleDeviation | Simple Deviation | a*d > exp(delta)*b*c |

Fisher | Fisher quantifier | The one-sided Fisher test rejects on the level alpha the null hypothesis of independence of A and S in favour of the alternative of their positive logarithmic interaction |

ChiSq | Chi-Square quantifier | The Chi-Square test asymptotically rejects on the level alpha the null hypothesis of independence of A and S in favour of the alternative of their positive logarithmic interaction |

EQ | E-quantifier | - |

ParaSeparation | Paraconsistent separation | Paraconsistent separation quantifier looking for (almost) separate A and S with truth-criterion of (1+p)*a<=b+c. It is recommend also to set-up a simple-frequency quantifier for (b+c) to assure some minimal sample size. |

BASE | BASE | a >= BASE ... at least BASE-number of objects for dependency to be statistically relevant |

CEIL | Ceiling | a <= CEIL ... not more than CEIL-number of objects (i.e. not too 'obvious' dependency) |

afrequency | a-frequency | a-frequency from contingency table |

bfrequency | b-frequency | b-frequency from contingency table |

cfrequency | c-frequency | c-frequency from contingency table |

dfrequency | d-frequency | d-frequency from contingency table |

rfrequency | r-frequency | r-frequency (a+b) from contingency table |

sfrequency | s-frequency | s-frequency (c+d) from contingency table |

kfrequency | k-frequency | k-frequency (a+c) from contingency table |

lfrequency | l-frequency | l-frequency (b+d) from contingency table |

adfrequency | ad-frequency | ad-frequency (a+d) from contingency table |

bcfrequency | bc-frequency | bc-frequency (b+c) from contingency table |

Sum | Sum of values | Sum of frequencies (n) from contingency table |

Min | Min value | Minimal frequency from contingency table |

Max | Max value | Maximal frequency from contingency table |

FreqDiffVal | Frequency difference | Number of rows matching both antecedent and succedent minus frequency of corresponding category in histogram |

FreqDiffRel | Relative frequencies difference | Difference of confidence and relative frequency of a corresponding category in histogram |

FreqDiffRelAbs | Absolute value of relative frequencies difference | Absolute value of difference of confidence and relative frequency of a corresponding category in histogram |

FreqDiffRelRatio | Ratio of relative frequencies | Lift, i.e. ratio of relative frequency of succedent in rows matching antecedent to a corresponding relative frequency in histogram |

FreqDiffCat | Relative frequencies of category difference | Difference of relative frequencies of category |

FreqDiffCatAbs | Absolute value of relative frequencies of category difference | Absolute value of relative frequencies of category |

FreqDiffCatRatio | Ratio of relative frequencies of category | Ratio of relative frequencies of category |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | |

RelAll | Relative [%] to all objects | |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal frequency in the current contingency table. Threshold value [%] is multiplied by (the highest frequency)/100 |

Key |
Name |
Note |

Positive | Positive | Positive gace |

Negative | Negative | Negative gace (negation) |

Both | Both | Both (positive and negative) |

Key |
Name |
Note |

InPolygonName | In-Polygon name | Name of the polygon the coordinates are within |

PointName | Closest-Point name | Name of the closest point to the coordinates |

PointDistance | Closest-Point distance | Distance [km] from the coordinates to the the closest point |

PointCount | Point-number | Number of points within a distance [km] from the coordinates (distance given by a threshold value) |

NotSet | Not set | Not applicable to this attribute |

Key |
Name |
Note |

Euclidean | Euclidean | Euclidean distance of cartesian planar coordinates. |

Cylindrical | Cylindrical | Euclidean distance on cylindrical projection of geographical coordinates (WGS84) and transformed into kilometers. Applicable for points up to 500 km apart and within latitudes of 70N and 70S. |

Haversine | Haversine | Haversine formula for computing distances on the sphere for geographical coordinates (WGS84) and transformed into kilometers. The better precison for large distances on the Earth, but slowest. |

NotSet | Not set | Not applicable to this attribute |

Key |
Name |
Note |

Finite | Finite value | Finite value (not infinity) |

PlusInfinity | Plus infinity | Plus infinity value |

MinusInfinity | Minus infinity | Minus infinity value |

Key |
Name |
Note |

From | From | Left border value |

To | To | Right border value |

FuzzyFromMin | Fuzzy from (min) | First left fuzzy border value |

FuzzyFromMax | Fuzzy from (max) | Second left fuzzy border value |

FuzzyToMin | Fuzzy to (min) | First right fuzzy border value |

FuzzyToMax | Fuzzy to (max) | Second right fuzzy border value |

Key |
Name |
Note |

Abs | Absolute number | Absolute frequencies. Frequencies in histogram are left as given. |

RelCondition | Relative [%] to act condition | Relative frequencies to number of rows matching condition. Frequencies are divided by number of rows matching condtion and multiplied by 100. |

RelRow | Relative [%] for each row | Relative frequencies for each row. Each frequency is divided by the sum of frequencies in its row and multiplied by 100. |

RelCol | Relative [%] for each column | Relative frequencies for each column. Each frequency is divided by the sum of frequencies in its column and multiplied by 100. |

Key |
Name |
Note |

Sum | Sum of frequencies | Sum of frequencies from given part of contingency table |

Min | Min frequency | Minimal frequency from given part of contingency table |

Max | Max frequency | Maximal frequency from given part of contingency table |

Avg | Average frequency | Average frequency from given part of contingency table |

Some | Some frequency | At least one frequency from given part of contingency table |

CramerV | Cramer's V coefficient | Cramer's V (association of two nominal variables) in <0;1> (the farther is value from 0 the more dependant) |

Kendall | Kendall's TauB coefficient | Kendall's TauB (coefficient of ordinal correlation of two variables) in <-1;1> (the farther is value from 0 the more dependant) |

ChiSq | Chi-square test | Chi-square test of similarity (the greater value the more dependant) |

Variation | Variation from pattern | Total variation from pattern |

ConditionalEntropy | Conditional entropy H(C|R) | Conditional entropy of columns given rows (the lower value the more dependant <0;log2(L)>) |

MutualInformation | Mutual information MI(R,C) normalized | Mutual information between rows and columns (the greater value the more dependant <0;1>) |

InformationDependence | Inf. dependence ID(R,C) | Information dependence between rows and columns (the greater value the more dependant <0;1>) |

AsymetricInformation | Asymetric information coefficient AIC(R,C) | Value of asymetric information coefficient Theta (the greater value the more dependant <0;1>) |

PattDiffSum | PattDiffSum | Sum of absolute values of differences of frequencies in the contingency table and a given pattern |

PattDiffMin | PattDiffMin | The minimal of absolute values of differences of frequencies in the contingency table and a given pattern |

PattDiffMax | PattDiffMax | The maximal of absolute values of differences of frequencies in the contingency table and a given pattern |

Key |
Name |
Note |

Abs | Absolute number | Absolute number. Threshold value is left as given. |

RelCondition | Relative [%] to act condition | |

RelAll | Relative [%] to all objects | |

RelFrequencyMax | Relative [%] to max frequency | Relative to maximal frequency in the whole contingency table. Threshold value [%] is multiplied by (the highest frequency in the whole KL-table)/100 |

Key |
Name |
Note |

Antecedent | Antecedent | Mapping attributes to antecedent |

Succedent | Succedent | Mapping attributes to succedent |

Condition | Condition | Mapping attributes to condition |

FirstSet | FirstSet | Mapping attributes to the firstset |

SecondSet | SecondSet | Mapping attributes to the secondset |

Quantifier | Quantifier | Mapping of quantifier |

Filter | Filter | Mapping of filtering |

Key |
Name |
Note |

NotSet | Not set | Influence not set yet |

DontCare | Don't care | Not interested in this combination |

ToBeMined | To be mined | Waiting for to be mined |

Processed | Being processed | Beeing processed |

Solved | Solved | Results obtained |

Key |
Name |
Note |

FTMiner | 4ftMiner | 4ftMiner Task |

CFMiner | CF-Miner | CF-Miner Task |

KLMiner | KL-Miner | KL-Miner Task |

SD4ftMiner | SD4ft-Miner | SD4ft-Miner Task |

SDCFMiner | SDCF-Miner | SDCF-Miner Task |

SDKLMiner | SDKL-Miner | SDKL-Miner Task |

Key |
Name |
Note |

Basic | Basic | Literal must be included in every cedent |

Remaining | Remaining | Literal is obligatory in cedent |

Key |
Name |
Note |

KMeans | k-Means / k-Mode | Flat level k-Means or k-Mode clustering |

RBKMeans | Repeated bisection k-Means / k-Mode | Top-down recursive bi-section clustering producing a tree of clusters |

HAC | Hierarchical Agglomerative Clustering | Bottom-up hierarchical agglomerative clustering using distance matrix |

Key |
Name |
Note |

CatMean | Mean | Mean of category-index values |

CatModus | Modus | The most frequent category |

Key |
Name |
Note |

Euclidean | Euclidean distance | Euclidean measure of distance between two objects |

Cosinus | Cosinus similarity | Cosinus measure of similarity between two objects |

SimpleMatch | Simple match | Simple match of categories (Sokal and Michener) |

Eskin | Eskin measure | Esking similarity for nominal variables (Eskin et al.) |

VariableEntropy | Variable Entropy | Variable Entropy similarity measure of nominal variables (Sulc) |

VariableMutability | Variable Mutability | Variable Mutability similarity measure of nominal variables (Sulc) |

Key |
Name |
Note |

Simple | Simple | Simple linkage of clusters (the minimal distance) |

Complete | Complete | Complete linkage of clusters (the maximal distance) |

Average | Average | Average linkage of clusters (the average distance) |

Key |
Name |
Note |

Deleting | Delete | Not including missing values |

Pesimistic | Pesimistic fill up | Pesimistic fill up of missing values |

Optimistic | Optimistic fill up | Pesimistic fill up of missing values |

Ignore | Ignore X-categories | Ignoring X-categories |

Key |
Name |
Note |

NotSet | Not set | Scope not set yet |

BackgroundKnowledge | Background knowledge | Dependency follows from the domain background knowledge |

DataSpecific | Data specific | Dependency follows from data anomalies |

Unknown | Unknown | Scope is unknown |

Key |
Name |
Note |

NotSet | Not set | Influence not set yet |

SomeInfluence | Some influence | There is some influence but not examined in detail |

PositiveInfluence | Positive influence | If the row attribute increases then the column attribute increases too |

NegativeInfluence | Negative influence | If the row attribute increases then the column attribute decreases |

PositiveFrequency | Positive frequency | If the row attribute increases then the relative frequency of objects satisfying column attribute increases |

NegativeFrequency | Negative frequency | If the row attribute increases then the relative frequency of objects satisfying column attribute decreases |

PositiveBoolean | Positive boolean | If truthfulness of the row attribute increases then relative frequency of true values of column attribute increases too |

NegativeBoolean | Negative boolean | If truthfulness of the row attribute increases then relative frequency of true values of column attribute decreases |

Functional | Functional | There is a strong function-like dependency |

None | None | No influence at all |

DontCare | Don't care about influence | There maybe some influence but we don't care |

Unknown | Unknown | There could be an influence, no details are known |

Key |
Name |
Note |

NotSet | Not set | Validity not set yet |

Proven | Proven | Dependency proven |

Rejected | Rejected | Dependency was rejected |

Unknown | Unknown | Validity not know |

Key |
Name |
Note |

None | None | Not set |

EqualNotEqual | Equal versus Not-equal | One category against all others. As many dichotimized attributes with categories '=' and '<>' as there is categories in the original attribute. Suitable for nominal attributes. |

UpToAbove | Up-to versus Above | Disjunction of all categories up to a certain category against all other categories. As many dichotomized attributes with categories '<=' and '>' as there is categories in the original attribute minus one. Suitable for ordinal and cardinal attributes. |

Key |
Name |
Note |

Random | Random | Column values generated randomly |

Init | Initialized by formula | Column values initially computed by a mathematical formula |

Update | Updated by formula | Column values continuously updated by a mathematical formula |

Copy | Copied from the underlying data | Column values initialized by copying values from the underlying data |

Key |
Name |
Note |

Evolution | Evolution | Generating artificial data using evolutionary algorithm |

Randomizer | Randomizer | Randomization of previously generated data using evolution algorithm |

Key |
Name |
Note |

Uniform | Uniform | Uniform distribution of values across possible range |

Gaussian | Gaussian | A bell-shaped distribution with defined mean value and standard deviation |

Manually | Manually user-specified | Manually specified frequencies (for enumerations only) |

Key |
Name |
Note |

Random | Random | Randomly generated values for each individual |

Permutation | Permutation | Permutation of a one-time pregenerated random values |

Key |
Name |
Note |

Enumeration | Enumeration | Column could contain values only from a pre-defined list |

Range | Range of values | Column with any value within a given range |

RangeEnum | Range with predefined values | Column with any value within a given range with some predefined values |

Key |
Name |
Note |

Simple | FirstSet versus SecondSet | Comparison of the first set versus the second set |

JoinedSet | FirstSet versus FirstSet and SecondSet | The second set is treated as a subset specification to the first set |

InverseSet | FirsetSet versus not FirstSet | The second set is inverse of the first set |

Key |
Name |
Note |

FirstSet | First set frequencies | Quantifier test applied to the first set data only |

SecondSet | Second set frequencies | Quantifier test applied to the second set data only |

DiffAbs | Delta of absolute frequencies values | Quantifier test applied to the frequency table computed by substracting of corresponding frequencies |

DiffRel | Delta of relative frequencies values | Quantifier test applied to the frequency table computed by substracting of corresponding relative frequencies |

ValDiff | Difference of interest-measures | Test applied to the difference of quantifier values computed separately from each frequency table |

ValDiffAbs | Absolute difference of interest-measures | Test applied to the absolute difference of interest-measures computed separately from each frequency table |

ValRation | Ratio of interest-measures | Test applied to the ratio of interest-measures computed separately from each frequency table |

ValRatioMax | Higher of ratios of interest-measures | Test applied to the higher of two ratios of interest-measures (im1/im2 or im2/im1) |

Key |
Name |
Note |

FTMiner | 4ft-Miner | 4ft-Miner task for 4ft-association rules with rich syntax |

CFMiner | CF-Miner | CF-Miner task for conditonal frequencies of a single multi-categorical attribute |

KLMiner | KL-Miner | KL-Miner task for conditional frequencies of two multi-categorical attributes |

SD4ftMiner | SD4ft-Miner | SD4ft-Miner task for set difference in terms of two 4ft-association rules |

SDCFMiner | SDCF-Miner | SDCF-Miner task for set difference in terms of two frequencies of a single attribute |

SDKLMiner | SDKL-Miner | SDKL-Miner task for set difference in terms of two frequencies of two multi-categorical attributes |

Ac4ftMiner | Ac4ft-Miner | Ac4ft-Miner task for 4ft-action rules |

ETreeMiner | ETree-Miner | ETree-Miner task for decision and exploration trees |

MClusterMiner | MCluster-Miner | MCluster-Miner task for clustering analysis |

KEx | Knowledge Explorer | Knowledge Explorer (KEx) task for classifications using machine learning |

Key |
Name |
Note |

TrainingSet | Use training set | Use same records as for training |

CrossValidation | Cross-validation | Cross-validating each of n-folds |

RandomSplit | Random split | Random split of available data in given ration |

Key |
Name |
Note |

Integer | Integer number | Integer number between -2E09 and 2E09 |

Float | Decimal number | Floating-point number with double precision |

String | Text | String of characters |

Boolean | Boolean | Boolean value of TRUE/FALSE |

DateTime | Date/Time | Value with date, time or both |

Key |
Name |
Note |

Error | Error | Errors only |

Warning | Warning | Include warnings |

Info | Info | Include calls to lm.logInfo |

Normal | Normal | Include all calls to lm.log |

Fine | Fine | Log calls of main functions |

Finer | Finer | Log calls of all functions with named paremeters |

Finest | Finest | for debugging purposes |

Key |
Name |
Note |

TaskPooler | TaskPooler | Add task to TaskPooler queue (no parallelization) |

ProcPooler | ProcPooler | Add task to ProcPooler queue (parallel procesing on multiple cores) |

GridPooler | GridPooler | Add task to GridPooler queue (distributed parallel processing) |

Key |
Name |
Note |

None | Not generated yet | For newly created tasks or after a change has been made to task description |

Waiting | Waiting | Waiting in the queue of TaskPooler, ProcPooler or GridPooler |

Running | Running | Running locally or on the grid |

Solved | Solved | Successfully finished |

Interrupted | Interrupted | Finished because of the HypothesesCountMax was reached or interrupted by user |

Failed | Failed | An error occured which caused the task run to stop |

Key |
Name |
Note |

Unknown | Unknown | Relationship between the hypothesis and given mutual influence could not be described |

Unrelated | Unrelated | The hypothesis and given mutual influence are not related |

DerivedFrom | DerivedFrom | Hypothesis is derived from the mutual influence (supports it) |

Extending | Extending | Hypothesis extends the mutual influence |

InConflictWith | InConflictWith | Hypothesis is in conflict with the mutual influence (is an exception to) |