cleverminer 1.0.5

Beyond apriori. Cleverminer is explainable AI (XAI) package for enhanced association rule mining (eARM). It implements the GUHA procedures that generalises apriori and association rules in many ways. Rules are based on categorical data that can be easily visualized and interpreted. Their if-then with probability allows easy deployment by human realized processes. Trully explainable knowledge mining.

Beyond apriori. Cleverminer is explainable AI (XAI) package for enhanced association rule mining (eARM). It implements the GUHA procedures that generalises apriori and association rules in many ways. Rules are based on categorical data that can be easily visualized and interpreted. Their if-then with probability allows easy deployment by human realized processes. Trully explainable knowledge mining.

In general, apriori is looking for rules {ItemSet} -> {Item} (Base, prob). GUHA goes further and instead of items (boolean attributes), list of categorial attributes and combination of values is searched on left and right hand side. Moreover, GUHA has much more possibilites and several other procedures.

To run cleverminer procedures, use dataframe with categorical variables only. Cleverminer prepares all variables and values for future reuse.

What's new:

1.0.5

• supports missing pandas functionalities and implements several automated data preprocessing
• listing of variables, ordering and labels
• automatically process conversion to numeric, integers and order float & integer variables
• fixed verbosity level prints

1.0.4

• sorting output rules

1.0.3

• UIC Miner introduced

1.0.2

• merge changes from 0.91 (data structure checks; as 1.0.0 was build from 0.0.90 so remaining features are merged now)

1.0.1

• new procedures get4fold, gethist, getquantifiers, getrulecount

1.0.0 - Major release, major rebuild from all views:

• data import reworked and fastened significantly
• much faster calculation (rule mining) in Py3.10 + next optimizations for rule mining are in place
• output structure is enhanced, fully structured output is available for post-processing (trace_cedent, cedent_struct in output)
• data can be read once and multiple tasks can be performed (.mine method)
• optimizations for sd4ft miner
• verbosity options available (run progress output has been changed)
• additional options available (able to override maximum number of categories)
• better formatting outputs (bugfix)
• data structure in output has changed

0.0.91 - detect error in datatypes in input data and correctly report it

0.0.90 - fix in displaying rules for 4ft-Miner, in CF-Miner: allowing relmax to be bounded from both sides (leq introduced), in SD4ft-Miner: allowing ratioconf to be bounded from both sides (leq introduced)

0.0.89 - quantifiers and output dictionary names change in favor of rules terminology (output: hypotheses->rules; hypo_id -> rule_id, quantifiers kept for compatibility old and new names, including variability (like frstbase -> also base1 is possible)

0.0.88 - print of task summary, hypo listing and individual hypothesis

0.0.87 - support for 'one category' added

0.0.86 - bugfixes (space search for optimized branch, able to switch off optimization, minimal cedent length bug for optimized search)

0.0.85 - bugfixes (row_count), checking input structure

0.0.84 - optimizations for conjunctions