Evaluation and optimization of frequent, closed and maximal association rule based classification

Shaharanee, Izwan Nizal Mohd; Hadzic, Fedja

doi:10.1007/s11222-013-9404-6

Cited by 12 publications

(12 citation statements)

References 26 publications

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“…On the contrary, the procedure based on ST produces a more stable variables selection which is not in favor to any specific variables criterion. This is in agreement with the claim in [19,25,26], of ST being fair towards handling of multi-valued variables.…”

Section: Comparing Symmetrical Tau (St) With Mutual Information (Mi)supporting

confidence: 91%

A Framework for Interestingness Measures for Association Rules with Discrete and Continuous Attributes Based on Statistical Validity

Shaharanee

Jamil

2015

IFIP Advances in Information and Communication Technology

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Assessing rules with interestingness measures is the pillar of successful application of association rules discovery. However, association rules discovered are large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this framework. Furthermore, considering that many interestingness measures exist, we propose and compare two established approaches in selecting relevant attributes for the rules prior to rule generation. The proposed strategy combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non-significant rules. In addition to that, we consider real world datasets which are characterized by the uniform and non-uniform data/items distribution with mixture of measurement level throughout the data/items. The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole.

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Section: Comparing Symmetrical Tau (St) With Mutual Information (Mi)supporting

confidence: 91%

A Framework for Interestingness Measures for Association Rules with Discrete and Continuous Attributes Based on Statistical Validity

Shaharanee

Jamil

2015

IFIP Advances in Information and Communication Technology

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“…The experimental results have demonstrated that the proposed framework managed to reduce a large number of non-significant and redundant rules while simultaneously preserving a relatively high level of accuracy. As part of the ongoing works (Shaharanee and Hadzic, 2013), the proposed framework is intended to be used to evaluate the differences between frequent, maximal and close patterns when used for classification tasks, and the effect of the confidence threshold.…”

Section: Discussionmentioning

confidence: 99%

Untitled

2014

APJITM

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Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task

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“…Park et al [25] proposed symmetrically balanced cross-entropy by considering the advantages of a symmetric J measure and balanced cross-entropy in association rule evaluation. Shaharani et al [26] presented a systematic evaluation of the rules of association identified on the basis of frequent, closed, and maximum detailed exploration algorithms by combining general measures of data mining and statistical interest and described the appropriate sequence of usage. This method can also remove redundant association rules.…”

Section: Evaluation Methods and Framework Of Association Rulesmentioning

confidence: 99%

An Improved Evaluation Methodology for Mining Association Rules

Bao

Mao

Zhu

et al. 2021

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At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application.

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Evaluation and optimization of frequent, closed and maximal association rule based classification

Cited by 12 publications

References 26 publications

A Framework for Interestingness Measures for Association Rules with Discrete and Continuous Attributes Based on Statistical Validity

A Framework for Interestingness Measures for Association Rules with Discrete and Continuous Attributes Based on Statistical Validity

Untitled

An Improved Evaluation Methodology for Mining Association Rules

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