TY - JOUR
T1 - Conjecturable knowledge discovery
T2 - A fuzzy clustering approach
AU - Huang, Tony Cheng Kui
AU - Hsu, Wu Hsien
AU - Chen, Yen Liang
N1 - Funding Information:
The authors would like to thank the Area Editor, Dr. Frank Klawonn, and anonymous reviewers for their helps and valuable comments to improve this paper. The first author, Dr. Tony Cheng-Kui Huang, also appreciate Krannert School of Management, Purdue University, providing the research resources to support the revision of this paper during his visiting period. This research was supported by the National Science Council of the Republic of China under the grant NSC 100-2410-H-194-019-MY2.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Classification
KW - Conjecturable rules
KW - Data mining
KW - Fuzzy clustering
UR - http://www.scopus.com/inward/record.url?scp=84879942628&partnerID=8YFLogxK
U2 - 10.1016/j.fss.2012.12.006
DO - 10.1016/j.fss.2012.12.006
M3 - 期刊論文
AN - SCOPUS:84879942628
SN - 0165-0114
VL - 221
SP - 1
EP - 23
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
ER -