Conjecturable knowledge discovery: A fuzzy clustering approach
Traditionally, clustering is the task of dividing objects into homogeneous clusters based on their degrees of similarity. As objects are assigned to clusters, users need to manually give descriptions for all clusters. Characterizing clusters by hand can consume a great deal of time of users. In addition, users sometimes have no specific idea as to how to explain the clustering results; thus, they might give inappropriate descriptions. A clustering technique is proposed to discover conjecturable rules, providing descriptions of clusters with a decision tree classification technique. Every cluster in a conjecturable tree is depicted by only one conjecturable rule. However, less-utilized rules are not necessarily trivial. In some real-life circumstances, there might be some clusters which can be depicted by two or more rules, namely, recessive conjecturable rules. For example, customers usually prefer to buy inexpensive red wines; however, on certain occasions, such for a birthday celebration, they will buy expensive wine. Therefore, we know that there are some people who generally belong to a low-value cluster but may simultaneously be assigned to a high-value one. In this study, we propose a new discovery model for mining conjecturable rules to reveal this type of knowledge. The experimental results show that our proposed model is able to discover conjecturable rules as well as recessive rules. The results of sensitivity analysis are also given for practitioners' reference. (C) 2012 Elsevier B.V. All rights reserved.
fuzzy clustering conjecturable rules classification data mining