Name
Abstract | ||
|---|---|---|
As a featured function of search engine, clustering display of search results has been proved an efficient way to organize the web resource. However, for a given query, clustering results reached by any user are totally identical. In this paper, we explored a user-friendly clustering scheme that automatically learns users’ interests and accordingly generates interest-centric clustering. The basis of this personal clustering is a keyword based topic identifier. Trained by users’ individual search histories, the identifier provides most of personal topics. Each topic will be the clustering center of the retrieved pages. The scheme proposed distinguishes the functionality of clustering from that of topic identification, which makes the clustering more personal and flexible. To evaluate the proposed scheme, we experimented with sets of synthetic data. The experimental results prove it an effective scheme for search results clustering. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11428862_111 | International Conference on Computational Science (3) |
Keywords | Field | DocType |
web search result,individual search history,clustering center,clustering display,effective scheme,personal clustering,proposed scheme,interest-centric clustering,user-friendly clustering scheme,clustering result,efficient user-oriented clustering,search result,search engine,synthetic data | Canopy clustering algorithm,Data mining,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Information retrieval,Computer science,Constrained clustering,Cluster analysis,Brown clustering | Conference |
Volume | ISSN | ISBN |
3516 | 0302-9743 | 3-540-26044-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||