An efficient algorithm for mining high utility patterns from incremental databases with one database scan.

High utility pattern mining has been actively researched as one of the significant topics in the data mining field since this approach can solve the limitation of traditional pattern mining that cannot fully consider characteristics of real world databases. Moreover, database volumes have been bigger gradually in various applications such as sales data of retail markets and connection information of web services, and general methods for static databases are not suitable for processing dynamic databases and extracting useful information from them. Although incremental utility pattern mining approaches have been suggested, previous approaches need at least two scans for incremental utility pattern mining irrespective of using any structure. However, the approaches with multiple scans are actually not adequate for stream environments. In this paper, we propose an efficient algorithm for mining high utility patterns from incremental databases with one database scan based on a list-based data structure without candidate generation. Experimental results with real and synthetic datasets show that the proposed algorithm outperforms previous one phase construction methods with candidate generation.