A Novel Method of Interestingness Measures for Association Rules Mining Based on Profit

Abstract

Association rules mining is an important topic in the domain of data mining and knowledge discovering. Some papers have presented several interestingness measure methods; the most typical areSupport,Confidence,Lift,Improve, and so forth. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. This paper proposes three new methods,Bi-lift, Bi-improve, andBi-confidence, forLift, Improve, and Confidence, respectively. Then, on the basis of utility function and the executing cost of rules, we propose interestingness function based on profit (IFBP) considering subjective preferences and characteristics of specific application object. Finally, a novel measure framework is proposed to improve the traditional one through experimental analysis. In conclusion, the new methods and measure framework are prior to the traditional ones in the aspects of objective criterion, comprehensive definition, and practical application.

An Attribution of Cyberattack using Association Rule Mining (ARM)

“…2) Support Count: The total number of occurrences of an itemset X and/or itemset Y is defined as support count 3) Support: Support [11] measure the usefulness of association rules. It is defined as a proportion of transactions in a dataset that contains the itemset.…”

“…(2) 4) Confidence: Confidence is importance because it can indicate the strength or the reliability of an association rules [11]. It is defined as the ratio of the number of transactions that include all items in a frequent itemset to the number of transactions that include all items in the subset.…”

“…(3) 5) Lift: The lift value is a measure of the importance of a rule [11]. The lift measures how frequent X and Y occur together than expected if they were statistically independent.…”

“…This measure can effectively quantify the correlation between the antecedents and the consequent for enormous association rules generated by ARM that can meet the aims of the researcher. The relevant and effective association rules are achievable through interestingness measure that includes objective measure and subjective measure [11]. The objective measures based on the statistical strengths or properties of the generated rules and subjective measures that are obtained from the user deduction www.ijacsa.thesai.org or interest of their problem domain.…”

Pattern Identification on Protein Sequences of Neurodegenerative Diseases Using Association Rule Mining

“…In this work, strong and interesting association rules were generated and selected by using constraints on various measures of interest and significance (i.e. support, confidence, lift and improve [15]).…”