Name
Abstract | ||
|---|---|---|
Our previous work proposed a clustering algorithm to cluster research documents automatically. It used Web hit counts of AND-search on two words as a document vector. Target documents are clustered with a result of k-means clustering method, in which cosine similarity is used to calculate a distance. This paper uses this algorithm to cluster twitter users. However, the twitter users have different characteristics from the research documents. Therefore, we investigate problems of the using our algorithm for twitter users and propose some ideas to resolve it. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/NBiS.2013.70 | Network-Based Information Systems |
Keywords | Field | DocType |
twitter user,twitter user biography,document vector,cluster research document,different characteristic,cosine similarity,clustering algorithm,previous work,k-means clustering method,research document,cluster twitter user | Data mining,Clustering high-dimensional data,Data stream clustering,Cosine similarity,Computer science,Document clustering,Pattern clustering,Cluster analysis,Word processing | Conference |
ISSN | ISBN | Citations |
2157-0418 | 978-1-4799-2509-4 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masaki Kohana | 1 | 31 | 14.06 |
| Shusuke Okamoto | 2 | 65 | 28.98 |
| Masaya Kaneko | 3 | 0 | 0.68 |