Title :
On linguistic-based clustering
Author :
Sugawara, Akira ; Kinoshita, Naohiko ; Endo, Yuta
Author_Institution :
Grad. Sch. of Syst. & Inf. Eng., Univ. of Tsukuba, Tsukuba, Japan
Abstract :
Clustering which is one of data mining techniques is a method automatically classifying data into some clusters. Various types of clustering methods based on mathematical models are proposed. We call these methods model-based clustering. We use clustering methods to know data structure. However, we do not know which methods we should select unless we know data structure. Therefore, we propose a new clustering method based on linguistic rules, that is, fuzzy reasoning. We call the new method linguistic-based clustering. It is available when we do not even know data structure. Moreover, the effectiveness is shown through numerical examples.
Keywords :
computational linguistics; data mining; data structures; fuzzy reasoning; pattern classification; data classification; data mining techniques; data structure; fuzzy reasoning; linguistic rules; linguistic-based clustering; mathematical models; model-based clustering; Clustering methods; Couplings; Data structures; Fuzzy reasoning; Indexes; Method of moments; Niobium; clustering; fuzzy reasoning; linguistic-based clustering;
Conference_Titel :
Granular Computing (GrC), 2014 IEEE International Conference on
Conference_Location :
Noboribetsu
DOI :
10.1109/GRC.2014.6982847