Name
Abstract | ||
|---|---|---|
Clustering Web session is an important aspect of Web usage mining. In this paper, we propose a new algorithm of Web session fuzzy clustering, which applies the t-bridge algorithm to fuzzy equivalence matrix clustering algorithm. This algorithm is proved to have better accuracy, fewer CPU time and better scalability than others by the experiments. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/NPC.2008.93 | NPC Workshops |
Keywords | Field | DocType |
fuzzy clustering,web session,fuzzy set theory,pattern clustering,clustering web session,fuzzy equivalence matrix clustering algorithm,matrix algebra,better scalability,fuzzy equivalence matrix,fewer cpu time,web usage mining,internet,web session fuzzy clustering,data mining,better accuracy,new algorithm,t-bridge algorithm,algorithm design and analysis,accuracy,clustering algorithms | Fuzzy clustering,Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Web mining,Data stream clustering,Correlation clustering,Affinity propagation,Computer science,Algorithm,Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-0-7695-3354-4 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zixiang Zhao | 1 | 15 | 5.50 |
| Kun Bai | 2 | 8 | 3.48 |