Name
Abstract | ||
|---|---|---|
In this paper a new method is presented and used in clustering document collections. This method is based on the one-dimensional arrays of Self-Organizing Map network (1-D SOM array). The main idea of this method is to obtain the clustering results by calculating the distances between every two adjacent MSPs (the most similar prototype to the input vector) of well trained 1-D SOM. The process is simple, easy to understand and unnecessary to give the number of clusters beforehand. The experimental results show that this method works well in clustering document collection. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICIS.2008.109 | ACIS-ICIS |
Keywords | Field | DocType |
document clustering method,main idea,1-d som array,1-d som,one-dimensional som,adjacent msps,clustering result,new method,clustering document collection,self-organizing map network,input vector,clustering algorithms,information science,data mining,document clustering,sequences,computer science,prototypes,frequency,neural networks,text analysis | k-medians clustering,Fuzzy clustering,Data mining,Cluster (physics),Correlation clustering,Document clustering,Computer science,Document handling,Cluster analysis,Single-linkage clustering | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Yu | 1 | 0 | 0.68 |
| Pilian He | 2 | 29 | 7.46 |
| Yushan Bai | 3 | 1 | 1.41 |
| Zhenlei Yang | 4 | 0 | 0.34 |