Efficient incremental high utility pattern mining based on pre-large concept

High utility pattern mining has been actively researched in recent years, because it treats real world databases better than traditional pattern mining approaches. Retail data of markets and web access information data are representative examples of the real world data. However, fundamental high utility pattern mining methods aiming static data are not proper for dynamic data environments. The pre-large concept based methods have efficiency compared to static approaches when dealing with dynamic data. There are several methods dealing with dynamic data based on the pre-large concept, but they have drawbacks that they have to scan original data again and generate many candidate patterns. These two drawbacks are the main issues of performance degradation. To handle these problems, in this paper, we suggest an efficient approach of pre-large concept based incremental utility pattern mining. The proposed method adopts a more proper data structure to mine high utility patterns in incremental environments. The state-of-the-art method performs a database scan operation many times, which is not suitable for incremental environments. However, our method needs only one scan, which is more suitable to process dynamic data compared to the state-of-the-art method. In addition, with the proposed data structure, high utility patterns can be mined in dynamic environments more efficiently than the former method. Experimental results on real datasets and synthetic datasets show that the proposed method has better performance than the former method.