Erasable pattern mining based on tree structures with damped window over data streams

Several pattern mining methods have been proposed to process dynamic data streams because the data generated in industrial fields is continually accumulated. Erasable pattern mining techniques for processing dynamic data streams are needed to discover erasable patterns from dynamic data streams. In previous erasable pattern mining approaches suggested for dynamic data streams, all data are considered to have the same importance regardless of its timestamp. However, dynamic data streams have the characteristic that the new data is relatively more significant than the old data. In erasable pattern mining, one of the desired techniques is an approach in consideration of such characteristic of data streams. For this reason, we propose an erasable pattern mining algorithm over dynamic data streams based on the damped window model. Since the suggested technique considers the new data more important than the previous data, it can find more useful erasable patterns. In addition, erasable pattern mining based on the damped window model is conducted efficiently by employing the tree and table structures. In performance test, we present that our pruning techniques remove unnecessary operations related to invalid erasable patterns efficiently from damped-window-based data streams. Performance evaluation results using real datasets and synthetic datasets show that the proposed approach has good performance with regard to as execution time, pattern generation, and scalability by comparing between the suggested technique and the state of the art algorithms.