Tree-Miner: Mining Sequential Patterns from SP-Tree

Abstract

Data mining is used to extract actionable knowledge from huge amount of raw data. In numerous real life applications, data are stored in sequential form, hence mining sequential patterns has been one of the most popular fields in data mining. Due to its various applications, across the past decades, a significant number of literature have addressed this problem and provided elegant solutions. In this paper we propose a novel tree data structure, SP-Tree, to store the sequence database in a new and efficient manner. Additionally, we propose a new mining algorithm Tree-miner to mine sequential patterns from SP-Tree. To further enhance the performance of our algorithm, we incorporate multiple pruning techniques and optimizations. As our SP-Tree stores the complete database, it can also be used for incremental and dynamic databases, tree-structure is particularly advantageous for interactive mining. We demonstrate how our SP-Tree based Tree-miner algorithm significantly outperforms all of the existing state-of-the-art algorithms, across 6 real life datasets. We conclude by discussing the possible extensions of our approach to other related fields of sequential data.