Name
Abstract | ||
|---|---|---|
Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25631-8_26 | AIRS |
Keywords | Field | DocType |
result clustering,retrieval result,odp-239 datasets,search engines present query,search engine,new approach,long list,cluster content,novel search result,prominent search result | Hierarchical clustering,Fuzzy clustering,Cluster labeling,Data mining,CURE data clustering algorithm,Correlation clustering,Information retrieval,Computer science,Brown clustering,Cluster analysis,Single-linkage clustering | Conference |
Volume | ISSN | Citations |
7097 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anil Turel | 1 | 3 | 0.40 |
| Fazli Can | 2 | 581 | 94.63 |