Title :
Attribute clustering with unknown cluster numbers
Author :
Hong, Tzung-Pei ; Liou, Yan-Liang ; Lee, Cho-Han
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung
Abstract :
In this paper, we try to select features based on attribute clustering without knowing the exact cluster numbers in advance. A similarity measure for a pair of attributes is first described, and an attribute clustering approach based on the CAST algorithm is then proposed to group the attributes into adequate number of clusters. The representative attributes found in the clusters are thus used for classification such that the whole feature space is greatly reduced. If the values of some representative attributes cannot be obtained from current environments for inference, some other possible attributes in the same clusters can also be used to achieve approximate inference results.
Keywords :
inference mechanisms; pattern classification; pattern clustering; CAST algorithm; approximate inference results; attribute clustering; unknown cluster numbers; Algorithm design and analysis; Clustering algorithms; Computer science; Data mining; Extraterrestrial measurements; Filters; Inference algorithms; Machine learning; Pattern recognition; CAST algorithm; attribute clustering; feature space; representative attribute; similarity measure;
Conference_Titel :
Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2383-5
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2008.4811716