Service clustering method based on description context feature words and improved GSDMM model
To address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight w...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021150/ |
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| _version_ | 1841539315055722496 |
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| author | Qiang HU Jiaji SHEN Guanghui JING Junwei DU |
| author_facet | Qiang HU Jiaji SHEN Guanghui JING Junwei DU |
| author_sort | Qiang HU |
| collection | DOAJ |
| description | To address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight was constructed.The words that fit well with the context of service description were extracted as the set of feature words for each service.Then, an improved GSDMM model with topic distribution probability correction factor was established to generate service representation vectors and achieve distribution probability correction for non-critical topic items.Finally, K-means++ algorithm was employed to cluster Web services based on these service representation vectors.Experiments were conducted on real Web services in Web site of Programmable Web.Experiment results show that the quality of service representation vectors generated by the proposed method is higher than of other topic models.Further, the performance of our clustering method is significantly better than other service clustering methods. |
| format | Article |
| id | doaj-art-e397ccf4fde84908950bfbf96b79ae03 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-e397ccf4fde84908950bfbf96b79ae032025-01-14T07:22:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-08-014217618759743542Service clustering method based on description context feature words and improved GSDMM modelQiang HUJiaji SHENGuanghui JINGJunwei DUTo address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight was constructed.The words that fit well with the context of service description were extracted as the set of feature words for each service.Then, an improved GSDMM model with topic distribution probability correction factor was established to generate service representation vectors and achieve distribution probability correction for non-critical topic items.Finally, K-means++ algorithm was employed to cluster Web services based on these service representation vectors.Experiments were conducted on real Web services in Web site of Programmable Web.Experiment results show that the quality of service representation vectors generated by the proposed method is higher than of other topic models.Further, the performance of our clustering method is significantly better than other service clustering methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021150/Web serviceservice clusteringtopic modelGSDMM |
| spellingShingle | Qiang HU Jiaji SHEN Guanghui JING Junwei DU Service clustering method based on description context feature words and improved GSDMM model Tongxin xuebao Web service service clustering topic model GSDMM |
| title | Service clustering method based on description context feature words and improved GSDMM model |
| title_full | Service clustering method based on description context feature words and improved GSDMM model |
| title_fullStr | Service clustering method based on description context feature words and improved GSDMM model |
| title_full_unstemmed | Service clustering method based on description context feature words and improved GSDMM model |
| title_short | Service clustering method based on description context feature words and improved GSDMM model |
| title_sort | service clustering method based on description context feature words and improved gsdmm model |
| topic | Web service service clustering topic model GSDMM |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021150/ |
| work_keys_str_mv | AT qianghu serviceclusteringmethodbasedondescriptioncontextfeaturewordsandimprovedgsdmmmodel AT jiajishen serviceclusteringmethodbasedondescriptioncontextfeaturewordsandimprovedgsdmmmodel AT guanghuijing serviceclusteringmethodbasedondescriptioncontextfeaturewordsandimprovedgsdmmmodel AT junweidu serviceclusteringmethodbasedondescriptioncontextfeaturewordsandimprovedgsdmmmodel |