An N-List-Based Approach for Mining Frequent Inter-Transaction Patterns

Abstract

Mining frequent inter-transaction patterns (ITPs) from large databases is both useful and of interest. Since frequent inter-transaction patterns (FITPs) are discovered across transactions in a transaction database (TD), the number of patterns is very large. Therefore, the mining time and memory usage are very high. Although several algorithms have been proposed for mining FITPs, they still require long runtime and high memory usage. Recent research shows that N-list-based approaches are very efficient for mining frequent patterns (FPs). Therefore, in this paper, we propose an N-list-based algorithm, called NL-ITP-Miner, to mine FITPs. In the proposed algorithm, we adopt the advantages of the N-list structure to build up the IT-PPC-tree. During the process of building the IT-PPC-tree, NL-ITP-Miner applies our proposed theorems to eliminate infrequent inter-transaction 1-patterns to reduce the search space. NL-ITP-Miner scans the database once to find frequent inter-transaction (FIT) 1-patterns for constructing the IT-PPC-tree, after that, the NL-ITP-Miner algorithm traverses this tree to generate frequent 1-patterns, FIT 1-patterns with their respective N-lists. Besides, we also propose effective pruning strategies that help NL-ITP-Miner to reduce the search space significantly and generate FITPs more quickly. Experiments show that NL-ITP-Miner outperforms the state-of-the-art algorithms for mining FITPs in terms of runtime and memory usage.

Keywords

• Data mining
• inter-transaction patterns
• Internet of Things
• pattern mining