An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data

Database is a repository of information. Retrieving automatic patterns from the database provide the requisite information and are in great demand in various domains of science and engineering. The effective pattern mining methods such as pattern discovery and association rule mining have been developed and find its applicability in a wide gamut ranging from science to medical to military and to engineering applications. Contemporary methods of retrieval such as pattern discovery and association rule mining algorithms are useful only for retrieving the data. The limitations of using these techniques are that they are unable to provide a complete association and relationship among the diverse patterns that is retrieved. This paper attempts a solution to the above limitation by designing a new algorithm (CFIM) which generates closed frequent patterns and its associated data concurrently. CFIM makes explicit the relationship between the patterns and its associated data.