Interactive Constrained Association Rule Mining

We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (queries) on the associations to be generated. Our approach is a combination of the integration of querying conditions inside the mining phase, and the incremental querying of already generated associations. We present several concrete algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second International Conference on Knowledge Discovery and Data Mining (DaWaK 2000

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Controlling False Positives in Association Rule Mining

Association rule mining is an important problem in the data mining area. It enumerates and tests a large number of rules on a dataset and outputs rules that satisfy user-specified constraints. Due to the large number of rules being tested, rules that do not represent real systematic effect in the data can satisfy the given constraints purely by random chance. Hence association rule mining often suffers from a high risk of false positive errors. There is a lack of comprehensive study on controlling false positives in association rule mining. In this paper, we adopt three multiple testing correction approaches---the direct adjustment approach, the permutation-based approach and the holdout approach---to control false positives in association rule mining, and conduct extensive experiments to study their performance. Our results show that (1) Numerous spurious rules are generated if no correction is made. (2) The three approaches can control false positives effectively. Among the three approaches, the permutation-based approach has the highest power of detecting real association rules, but it is very computationally expensive. We employ several techniques to reduce its cost effectively.Comment: VLDB201

Association Rule Mining

Tato bakalářská práce se zabývá dolováním asociačních pravidel. První část se věnuje vysvětlení technologie dolování dat a teorie, kterou je dobré znát pro seznámení se s asociační analýzou. Další část se věnuje samotné asociační analýze a podrobně vysvětluje principy algoritmu Apriori. Poslední část práce popisuje implementaci a testování algoritmu Apriori v programovacím jazyce Java.This bachelor's thesis is concerned with the association rule mining. The first part is devoted to the explanation of data mining technology and theory, which are necessary pre-steps for getting acquainted with association analysis. The next part focuses on the association analysis itself and explains the principals of algorithm Apriori in detail. The last part of the thesis describes the implementation and testing of algorithm Apriori in the Java programming language.