Name
Abstract | ||
|---|---|---|
Data clustering is a technique for grouping similar data items together for convenient understanding. Conventional data clustering methods, including agglomerative hierarchical clustering and partitional clustering algorithms frequently perform unsatisfactorily for large text article collections, as well as the computation complexity of the conventional data clustering methods increase very quick with the number of data items. This paper presents a system for automatic document clustering by identifying topic keyword clusters of the text corpus. The proposed system adopts a multi-stage process. First, an aggressive data cleaning approach is employed to reduce the noise in the free text and further identify the topic keywords within the documents. All extracted keywords are then grouped into topic keyword clusters using the k-nearest neighbor graph approach and the keyword clustering function. Finally, all documents in the corpus are clustered based on the topic keyword clusters. The proposed method was assessed against conventional data clustering methods on a Web news collection, indicating that the proposed method is an efficient and effective clustering approach. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICITA.2005.303 | IEICE Transactions |
Keywords | DocType | Volume |
agglomerative hierarchical clustering,keyword clustering function,aggressive data,topic keyword clusters,partitional clustering,data item,web news collection,poor clustering result,multistage process,automatic document clustering,computational complexity,computation complexity,partitional clustering algorithm,topic keyword cluster,data clustering,k-nearest neighbor graph,graph theory,effective clustering approach,keyword clustering technique,document handling,conventional data,merging,clustering algorithms,noise reduction,information management,data mining | Conference | 1 |
Issue | ISBN | Citations |
8 | 0-7695-2316-1 | 17 |
PageRank | References | Authors |
1.11 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsi-Cheng Chang | 1 | 24 | 2.38 |
| Chiun-Chieh Hsu | 2 | 17 | 1.11 |