DRL-Prefixspan: A novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns

Abstract

Discovering sequential patterns is a rather well-studied area in data mining and has been found many diverse applications, such as basket analysis, telecommunications, etc. In this article, we propose an efficient algorithm that incorporates constraints and promotion-based marketing scenarios for the mining of valuable sequential patterns. Incorporating specific constraints into the sequential mining process has enabled the discovery of more user-centered patterns. We move one step ahead and integrate three significant marketing scenarios for mining promotion-oriented sequential patterns. The promotion-based market scenarios considered in the proposed research are 1) product Downturn, 2) product Revision and 3) product Launch (DRL). Each of these scenarios is characterized by distinct item and adjacency constraints. We have developed a novel DRL-PrefixSpan algorithm (tailored form of the PrefixSpan) for mining all length DRL patterns. The proposed algorithm has been validated on synthetic sequential databases. The experimental results demonstrate the effectiveness of incorporating the promotion-based marketing scenarios in the sequential pattern mining process.

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