KDD procedures


KDD procedures and modules

There are procedures for input data transformations, several data mining procedures based on the GUHA principle and machine learning procedure KEX. Some of procedures have additional modules.

All procedures and modules need subsystem Elementary. Both data mining procedures and machine learning procedure KEX have important common features.

LISp-Miner All-in-one
All-in-one installation file for the most used modules of the LISp-Miner system
LM Exec
LM Exec module for executing scripts in the LISp-Miner Control Language
LM GridPooler
Batch processing of tasks on the grid
LM Reverse-Miner
The Reverse-Miner module generates artificial data for educational and research purposes that could be later analysed in the usual way by the LISp-Miner system (or any other system for KDD).
Machine learning procedure KEX (Knowledge EXplorer) performs symbolic empirical multiple concept learning from examples, where the induced concept description is represented as a set of weighted decision rules.
LM Knowledge
LISp-Miner modules concerning KnowledgeBase and Analytical questions
Other files for download
LISp-Miner Legacy modules
All the LISp-Miner older modules prior to introduction of the LM Workspace
Data mining procedure 4ft-Miner mines for various types of association rules. Rules based on statistical hypothesis test and conditional association rules are also included. There is a demonstration of the main features of the procedure 4ft-Miner.
4ft-IL is an additional module of the 4ft-Miner procedure. It allows analysis of importance particular literals of found association rules.
4ft-UF is an additional module of the 4ft-Miner procedure. It allows sorting of found association rules according to individual preferences of interestingness.
4ft-AR2NL is an additional module of the 4ft-Miner procedure. It automatically converts some of association rules into natural language form.
Data mining procedure CF-Miner mines for conditions under which frequencies of categories of an attribute satisfy given requirements.
Data mining procedure KL-Miner mines for patterns based on evaluation of two-dimensional contingency tables.
KL-COLLAPS is an additional module of the KL-Miner procedure. It allows deeper analysis of found patterns concerning two – dimensional contingency table.
Data mining procedure MC-Miner clusters data in sense of a GUHA procedure paradigm (all possible clustering variants on given list of attributes and an interval of target number of clusters).
Data mining procedure ETree-Miner mines for exploration-tree structures.
Data mining procedure SD4ft-Miner mines for couples of sets that differ significantly in respect to some association rule.
Data mining procedure Action4ft-Miner mines for action rules.
Data mining procedure SDCF-Miner mines for couples of sets that differ significantly in respect to conditional frequencies of categories of an attribute under some condition.
Data mining procedure SDKL-Miner mines for couples of sets that differ significantly in respect to a pair of categorial attributes under some condition.
LM TaskPooler
Batch processing of tasks in the background
Data Preprocessing
The Data Preprocessing subsystem consists of empty metabase, module Admin and of data exploration and transformation procedure DataSource.
Data transformation procedure TimeTransf computes various characteristics of time series. The resulting characteristics can be used as input of analytical procedures.

Print page


Send comments about this site to the webmaster