Name
Abstract | ||
|---|---|---|
Many popular web service networks are content-rich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose Service Cluster, a novel heterogeneous service network clustering algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration. Extensive evaluation on real datasets demonstrates that Service Cluster outperforms existing representative methods in terms of both effectiveness and efficiency. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICWS.2015.43 | International Conference on Web Services |
Keywords | Field | DocType |
clustering service networks,entity heterogeneity,attribute heterogeneity,link heterogeneity,Web service networks,heterogeneous graph clustering techniques,Service Cluster,heterogeneous service network clustering algorithm,discrete steepest descent method,dynamic learning method,optimization strategy | Data mining,Fuzzy clustering,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Theoretical computer science,Constrained clustering,Cluster analysis | Conference |
Citations | PageRank | References |
2 | 0.36 | 27 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Zhou | 1 | 606 | 34.54 |
| Ling Liu | 2 | 2181 | 142.51 |
| Calton Pu | 3 | 5377 | 877.83 |
| Xianqiang Bao | 4 | 8 | 3.29 |
| Kisung Lee | 5 | 342 | 27.05 |
| Balaji Palanisamy | 6 | 400 | 36.26 |
| Emre Yigitoglu | 7 | 38 | 4.01 |
| Qi Zhang | 8 | 113 | 15.49 |