Discovering Chronic-Frequent Patterns in Transactional Databases

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.