Paper Info
Title
A Probabilistic Model for Service Clustering - Jointly Using Service Invocation and Service Characteristics
Abstract
Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships among service users. In fact, mutual invocation relationships between services occur on operations of the corresponding services, while service attributes are the whole service description. Our main challenge may be to effectively combine these two kinds of data for service clustering. To address this issue, we propose a new probabilistic generative model which contains two closely connected parts, one characterizing operation community memberships by using operation invocation relationships, and the other characterizing service cluster memberships by utilizing service attributes. The correlations between these two parts are characterized by the relationships between operation communities and service clusters. To train this model, we provide a nested expectation-maximization algorithm. Experimental results show its superior performance over the existing methods for service clustering.
Year
DOI
Venue
2018
10.1109/ICWS.2018.00047
2018 IEEE International Conference on Web Services (ICWS)
Keywords
Field
DocType
service clustering,generative model,EM algorithm
Data mining,Computer science,Invocation,Expectation–maximization algorithm,Probabilistic generative model,Statistical model,Service discovery,Cluster analysis,Generative model
Conference
ISBN
Citations 
PageRank 
978-1-5386-7248-8
1
0.40
References 
Authors
3
7
Name
Order
Citations
PageRank
Dongxiao He120128.10
Xue Yang21510.21
zhiyong feng35520.86
Shizhan Chen46722.79
Keman Huang524222.29
Zhenzhu Wang610.40
F. Fogelman Souli'e7175.04