Please use this identifier to cite or link to this item:
|Title:||Using cluster validation criterion to identify optimal feature subset and cluster number for document clustering|
|Keywords:||Cluster number estimation|
|Citation:||Niu, Z.-Y., Ji, D.-H., Tan, C.L. (2007). Using cluster validation criterion to identify optimal feature subset and cluster number for document clustering. Information Processing and Management 43 (3) : 730-739. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ipm.2006.07.022|
|Abstract:||This paper presents a cluster validation based document clustering algorithm, which is capable of identifying an important feature subset and the intrinsic value of model order (cluster number). The important feature subset is selected by optimizing a cluster validity criterion subject to some constraint. For achieving model order identification capability, this feature selection procedure is conducted for each possible value of cluster number. The feature subset and the cluster number which maximize the cluster validity criterion are chosen as our answer. We have evaluated our algorithm using several datasets from the 20Newsgroup corpus. Experimental results show that our algorithm can find the important feature subset, estimate the cluster number and achieve higher micro-averaged precision than previous document clustering algorithms which require the value of cluster number to be provided. © 2006 Elsevier Ltd. All rights reserved.|
|Source Title:||Information Processing and Management|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 20, 2018
WEB OF SCIENCETM
checked on Apr 18, 2018
checked on Apr 21, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.