Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining
Autor: | Bilal Alatas, Elif Varol Altay |
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Rok vydání: | 2021 |
Předmět: |
Information Systems and Management
Association rule learning Computer science 05 social sciences Process (computing) 050301 education 02 engineering and technology computer.software_genre Computer Science Applications Theoretical Computer Science Set (abstract data type) Sine cosine algorithm Data set Task (computing) Artificial Intelligence Control and Systems Engineering Differential evolution 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining 0503 education computer Software |
Zdroj: | Information Sciences. 554:198-221 |
ISSN: | 0020-0255 |
Popis: | In association rules mining from data that have numeric-valued attributes, automatically adjusting the attribute intervals at the time of the mining process without a preprocess is very critical for preventing data loss and attribute interactions. In this paper, differential evolution and sine cosine algorithm based novel hybrid multi-objective evolutionary optimization methods are proposed for rapidly and directly mining the reduced high-quality numerical association rules by simultaneously adjusting the relevant intervals of related attributes without finding the frequent itemsets. These algorithms perform a global search and find the high-quality rules set in only one execution by modeling the rule mining task as a multi-objective problem that simultaneously meets different conflicting metrics. The algorithms proposed in this paper ensure the discovered rules to have high confidence and support and to be comprehensible. The proposed methods automate the rule mining process by directly finding the minimum intervals for the attributes and eliminating the need for minimum confidence and minimum support determined beforehand for each data set. The performances of new algorithms proposed in this study were tested with those of the state-of-the-art algorithms. The results show superiority of the proposed methods on the data sets that contain fewer attributes and higher number of instances. |
Databáze: | OpenAIRE |
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