2015 | OriginalPaper | Buchkapitel
Discovering Chronic-Frequent Patterns in Transactional Databases
verfasst von : R. Uday Kiran, Masaru Kitsuregawa
Erschienen in: Databases in Networked Information Systems
Verlag: Springer International Publishing
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This paper investigates the partial periodic behavior of the frequent patterns in a transactional database, and introduces a new class of user-interest-based patterns known as chronic-frequent patterns. Informally, a frequent pattern is said to be
chronic
if it has sufficient number of cyclic repetitions in a database. The proposed patterns can provide useful information to the users in many real-life applications. An example is finding chronic diseases in a medical database. The chronic-frequent patterns satisfy the anti-monotonic property. This property makes the pattern mining practicable in real-world applications. The existing pattern growth techniques that are meant to discover frequent patterns cannot be used for finding the chronic-frequent patterns. The reason is that the tree structure employed by these techniques’ capture only the frequency and disregards the periodic behavior of the patterns. We introduce another pattern-growth algorithm which employs an alternative tree structure, called Chronic-Frequent pattern tree (CFP-tree), to capture both frequency and periodic behavior of the patterns. Experimental results show that the proposed patterns can provide useful information and our algorithm is efficient.