A New Data Stream Mining Algorithm for Interestingness-Rich Association Rules

Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the associat...

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Veröffentlicht in:The Journal of computer information systems Jg. 53; H. 3; S. 14 - 27
1. Verfasser: Kuthadi, Venu Madhav
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stillwater Taylor & Francis 01.03.2013
Taylor & Francis Ltd
Schlagworte:
Algorithm
Algorithms
Data mining
Data Stream
Data Stream Parameters-tuned Interestingness (DSPI)
Dimensional analysis
Frequency Supporters-tuned Interestingness (FSI)
Interestingness
Parameters-tuned Interestingness (API)
Probability distribution
Process controls
Streaming media
Studies
Surveillance
ISSN:0887-4417, 2380-2057
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Zusammenfassung:Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. proficiency of the mined rules. However, fewer algorithms exist in the literature to deal with the utility as most of them deals with reducing the complexity in frequent itemset/association rules mining algorithm. Also, those few algorithms consider only the overall utility of the association rules and not the consistency of the rules throughout a defined number of periods. To solve this issue, in this paper, an enhanced association rule mining algorithm is proposed. The algorithm introduces new weightage validation in the conventional association rule mining algorithms to validate the utility and its consistency in the mined association rules. The utility is validated by the integrated calculation of the cost/price efficiency of the itemsets and its frequency. The consistency validation is performed at every defined number of windows using the probability distribution function, assuming that the weights are normally distributed. Hence, validated and the obtained rules are frequent and utility efficient and their interestingness are distributed throughout the entire time period. The algorithm is implemented and the resultant rules are compared against the rules that can be obtained from conventional mining algorithms.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0887-4417
2380-2057
DOI:10.1080/08874417.2013.11645628