Offering pattern mining using high yield partition trees

Abstract

Despite the wide use of data mining techniques in client segmentation and market analysis applications, so far there have been no algorithms that allow for the discovery of strategically important combinations of products (or offerings) - the ones that have the highest impact on the performance of the company. We present a novel algorithm for analyzing a multi-product environment and identifying strategically important combinations of offerings with respect to a predefined criterion, such as revenue impact, profit impact, inventory turnover etc. In contrast to the traditional association rule and frequent item mining techniques, the goal of the new algorithm is to find segments of data, defined through combinations of products (rules), which satisfy certain conditions as a group. We present a novel algorithm to derive specialized partition threes, called High Yield Partition Trees, which lead to such segments, and investigate different splitting strategies. The algorithm has been tested on real-world data, and achieved very good performance. © 2006 IEEE.