Name
Abstract | ||
|---|---|---|
Edit distance, as a similarity measure between user traversal patterns, satisfies the need of varying-length of user traversal sequences very well because it can be computed between different-length symbol strings which needs lower time and storage expense. Moreover, web topology is skillfully used to compute the relationship between pages which is used as a measure of cost of an edit operation. Finally, two-threshold sequential clustering method (TTSCM) is used to cluster user traversal patterns avoiding specifying the number of cluster in advance, and reducing the dependency between the clustering results and the clustering order of traversal patterns. Experimental results test and verify the effectiveness and flexibility of our proposed methods. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24273-1_60 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Edit distance,Clustering,Traversal Pattern,Web Topology | Edit distance,Data mining,Tree traversal,Graph traversal,Similarity measure,Computer science,Theoretical computer science,Cluster analysis | Conference |
Volume | Issue | ISSN |
238 | null | 1865-0929 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoqiu Tan | 1 | 11 | 1.65 |
| Miaojun Xu | 2 | 5 | 0.78 |