|Title||Mining Conditional Contrast Patterns|
|Publication Type||Book Chapter|
|Year of Publication||2009|
|Authors||Guozhu Dong, Guimei Liu, Limsoon Wong, Jinyan Li|
This chapter considers the problem of 'conditional contrast pattern mining.' It is related to contrast mining, where one considers the mining of patterns/models that contrast two or more datasets, classes, conditions, time periods, and so forth. Roughly speaking, conditional contrasts capture situations where a small change in patterns is associated with a big change in the matching data of the patterns. More precisely, a conditional contrast is a triple (B, F1, F2) of three patterns; B is the condition/context pattern of the conditional contrast, and F1 and F2 are the contrasting factors of the conditional contrast. Such a conditional contrast is of interest if the difference between F1 and F2 as itemsets is relatively small, and the difference between the corresponding matching dataset of B∪F1 and that of B∪F2 is relatively large. It offers insights on 'discriminating' patterns for a given condition B. Conditional contrast mining is related to frequent pattern mining and analysis in general, and to the mining and analysis of closed pattern and minimal generators in particular. It can also be viewed as a new direction for the analysis (and mining) of frequent patterns. After formalizing the concepts of conditional contrast, the chapter will provide some theoretical results on conditional contrast mining. These results (i) relate conditional contrasts with closed patterns and their minimal generators, (ii) provide a concise representation for conditional contrasts, and (iii) establish a so-called dominance-beam property. An efficient algorithm will be proposed based on these results, and experiment results will be reported. Related works will also be discussed.
|Full Text|| |
Guozhu Dong, Jinyan Li, Guimei Liu, Limsoon Wong. Mining Conditional Contrast Patterns. Book chapter in: Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction. Editors: Yanchang Zhao, Chengqi Zhang and Longbing Cao. IGI Global.