Sequential Pattern Mining for Library System using Prefix Spanning

Abstract

Sequential pattern mining is an important data mining problem with broad applications. Most of the sequential pattern mining methods, such as GSP (Generalized Sequential Pattern) and AprioriAll explore a candidate generation and test approach to reduce the number of candidates to be examined. These approaches may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. The better algorithm for sequential pattern is based on pattern-growth, a divide-and-conquer algorithm that projects and partitions databases based on the currently identified frequent patterns and grow such patterns to longer ones using the projected databases. This paper presents mining sequential pattern from library database by prefixSpan algorithm, which explores prefix projection in sequential pattern mining. PrefixSpan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover, prefix-projection substantially reduces the size of projected databases and leads to efficient processing. The experimental results of our system are also discussed in this paper.