NDLI: Towards efficient discovery of coverage patterns in transactional databases

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Towards efficient discovery of coverage patterns in transactional databases

Content Provider	ACM Digital Library
Author	Kiran, R. Uday Kitsuregawa, Masaru Toyoda, Masashi
Abstract	Coverage pattern mining is an important model in data mining. It provides useful information pertaining to the sets of items that have coverage interesting to the users in a transactional database. The coverage patterns do not satisfy the anti-monotonic property. This increases the search space in the itemset lattice, which in turn increases the computational cost of mining these patterns. An Apriori-like algorithm known as CMine has been proposed in the literature to discover the patterns. It employs a pruning technique to reduce the search space. We have observed that there exists further scope for reducing the search space effectively. In this paper, we theoretically analyze different measures used in the pattern model, and introduce a novel pruning technique to reduce the search space. An Apriori-like algorithm, called CMine++, has also been proposed to discover the patterns. The performance study shows that mining coverage patterns with CMine++ is efficient.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450319218
DOI	10.1145/2484838.2484850
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-07-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Knowledge discovery Coverage set and pattern mining Data mining
Content Type	Text
Resource Type	Article

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