Exploiting high utility occupancy patterns

Abstract

Most studies have considered the frequency as sole interestingness measure for identifying high quality patterns. However, each object is different in nature, in terms of criteria such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, and may not be truly representative. Thus, this paper extends the occupancy measure to assess the utility of patterns in transaction databases. The High Utility Occupancy Pattern Mining (HUOPM) algorithm considers user preferences in terms of frequency, utility, and occupancy. Several novel data structures are designed to discover the complete set of high quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of HUOPM.