Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Abstract

Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem, the high utility pattern mining approaches from non-binary database have been proposed and actively studied by many researchers. Lately, new data is progressively created with the passage of time in diverse area such as biometric data of a patient diagnosed in a medical device and log data of an internet user, and the volume of a database is gradually increasing. A database with these characteristics is called a dynamic database. Under these circumstances, high utility mining techniques suitable for analyzing dynamic databases have recently been extensively studied. In this paper, we propose a new list-based algorithm that mines high utility patterns considering the arrival time of each transaction in an incremental database environment. That is, our algorithm efficiently performs pattern pruning by using a damped window model that considers the importance of the previously inputted data lower than that of recently inserted data and identifies high utility patterns. Experimental results indicate that our proposed method has better performance than the state-of-the-art techniques in terms of runtime, memory, and scalability.

Keywords

• Data mining
• damped window model
• pattern pruning
• high utility patterns
• stream data mining