Affiliations: Department of Computer Engineering, Sejong University, Neungdong-ro, Gwangjin-gu, Seoul, Korea
Note: [] Corresponding author. Unil Yun, Department of Computer Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, Korea. Tel.: +82 2 3408 2902; Fax: +82 2 3408 4321; E-mail: yunei@sejong.ac.kr
Abstract: Erasable pattern mining is one of the variations in frequent pattern mining, and its main goal is to maximize the production capacity of manufacturing industries by quickly overcoming the financial crises that can occur in industries. However, existing erasable pattern mining approaches utilize only profit information for each product, but do not consider distinct weights of items organizing the products. In addition, previous algorithms spend much time and space mining erasable patterns due to their inefficient pattern mining methods. In a real-life environment, considering weights of items in each product can be more important compared to calculating product profits only. For this reason, we propose a novel algorithm for mining weighted erasable patterns by considering the distinct weight of each item. Moreover, we discuss both discovering weighted erasable patterns and minimizing the resource availability of erasable pattern mining processes utilizing weight conditions. Especially, our approach has advantages for time and space resource consumption compared to existing approaches because our algorithm uses a pattern pruning method and stores item information included in product databases by using both compact tree and hash list structures. We present performance evaluation utilizing both real and synthetic datasets to demonstrate the efficiency of our algorithm.
Keywords: Data mining, erasable pattern mining, weighted pattern mining, weight-based constraints, weighted gain
