A service clustering method based on wisdom of crowds

Jie, Wei ORCID: https://orcid.org/0000-0002-5392-0009 (2019) A service clustering method based on wisdom of crowds. In: IEEE BigData Congress 2019, 8-13 July 2019, Milan, Italy. (In Press)

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Abstract

As the number and variety of services increase, it is becoming difficult and time-consuming to locate services that satisfy users’ need. Service clustering is efficacious method to prune the query space, in order to narrow the search space, and improve the accuracy of locating services that satisfied users’ needs. At present, clustering method of web services adopted single or traditional clustering algorithms. However, accuracy and stability of single or traditional clustering algorithms is poor. In this paper, we proposed SWOC a service clustering method based on wisdom of crowd. Firstly, by using SWOC we calculated document similarity. Secondly, we implemented a mapping algorithm that reduces the correlation of web services and improve accuracy of method. And then, we applied different number of clusters using different individual clustering methods that increase the number of partitions in order to enhance the robustness of SWOC. Lastly, the diversity algorithm evaluates and selects the partitions to extract interesting information for the final aggregation with the weight of each individual result. Experiments were conducted on the real web service dataset crawled from ProgrammableWeb which demonstrate the accuracy, recall, F-value and stability of proposed method.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Acknowledgement: This work is supported by the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No.2018KW-049), the Special Scientific Research Program of the Education Department of Shaanxi Province, China (Grant No.17JK0711), the National Natural Science Foundation of China (61702414) and Natural Science Basic Research Program of Shaanxi (Grant No. 2018JQ6078).
Uncontrolled Keywords: cluster; service clustering; wisdom of crowds; clustering analysis; clustering ensemble
Subjects: Computing
Depositing User: WEI JIE
Date Deposited: 02 May 2019 09:04
Last Modified: 02 May 2019 09:04
URI: http://repository.uwl.ac.uk/id/eprint/6027

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