Efficient Mining of Active and Valuable Clustered Sequential Patterns

Abstract

Clustering of inherent sequential natured data sets is useful for various purposes. Over the years, many methods have been developed for clustering objects having sequential nature according to their similarity. However, these methods tend to have a computational complexity that is at least quadratic on the number of sequences. Also, clustering algorithms often require that the entire dataset be kept in the computer memory. In this paper, we present novel algorithm for Mining of constraint based clustered sequential patterns (CBCSP) algorithm for clustering only user interesting sequential data using recency, monetary and compactness constraints. So, the algorithm generates a compact set of clusters of sequential patterns according to user interest by applying constraints in mining process. It minimizes the I/O cost involved. The proposed algorithm basically applies the well known K-means clustering algorithm along with Prefix-Projected Database construction to the set of sequential patterns. In this approach, the method first performs clustering based on a novel similarity function and then captures the sequential patterns of which are only user interesting in each cluster using a sequential pattern mining algorithm which employs pattern growth method not. The proposed work results in reduced search space as user intended sequential patterns tend to be discovered in the resulting list. Through experimental evaluation under various simulated conditions, the proposed method is shown to deliver excellent performance and leads to reasonably good clusters.