基于描述语境特征词与改进GSDMM模型的服务聚类方法

doi:10.11959/j.issn.1000-436x.2021150

Abstract

Abstract:

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.

Key words: Web service, service clustering, topic model, GSDMM

CLC Number:

TN92

Qiang HU, Jiaji SHEN, Guanghui JING, Junwei DU. Service clustering method based on description context feature words and improved GSDMM model[J]. Journal on Communications, 2021, 42(8): 176-187.

Figures/Tables 16

数据集	主题模型	SC₁	SC₂	DBI₁	DBI₂
	R_GSDMM	0.882	$0 . 908$	0.637	$0 . 546$
	GSDMM	0.857	0.892	0.765	0.621
	LDA	0.230	0.395	1.579	1.401
DS₁	LSA	0.073	0.145	1.952	1.493
	LDA_W2V	0.225	0.358	1.106	0.781
	BTM	0.227	0.272	1.391	1.308
	HDP	0.331	0.247	0.840	1.123
	R_GSDMM	0.870	$0 . 905$	0.696	$0 . 547$
	GSDMM	0.851	0.893	0.774	0.695
	LDA	0.192	0.326	1.660	1.499
DS₂	LSA	0.063	0.146	2.002	1.482
	LDA_W2V	0.184	0.215	1.235	1.156
	BTM	0.195	0.239	1.472	1.393
	HDP	0.361	0.300	0.770	1.079
	R_GSDMM	0.842	$0 . 896$	0.812	$0 . 557$
	GSDMM	0.826	0.863	0.843	0.808
	LDA	0.142	0.182	1.907	1.841
DS₃	LSA	0.042	0.130	2.218	1.564
	LDA_W2V	0.116	0.119	1.554	1.560
	BTM	0.150	0.189	1.679	1.565
	HDP	0.385	0.335	0.788	0.957

主题模型	AMI₁	AMI₂	NMI₁	NMI₂	ARI₁	ARI₂
R_GSDMM	0.349	$0 . 443$	0.371	$0 . 465$	0.219	$0 . 274$
GSDMM	0.317	0.364	0.334	0.389	0.206	0.183
BTM	0.266	0.345	0.294	0.384	0.145	0.168
LDA	0.106	0.186	0.123	0.209	0.072	0.101
LSA	0.292	0.317	0.366	0.385	0.113	0.118
LDAW2V	0.018	0.022	0.031	0.036	0.007	0.009
HDP	0.033	0.066	0.048	0.107	0.015	0.012

主题模型	AMI₁	AMI₂	NMI₁	NMI₂	ARI₁	ARI₂
R_GSDMM	0.669	$0 . 688$	0.682	$0 . 704$	0.520	$0 . 524$
GSDMM	0.634	0.659	0.653	0.686	0.453	0.471
BTM	0.286	0.335	0.408	0.436	0.089	0.132
LDA	0.368	0.499	0.378	0.520	0.267	0.384
LSA	0.548	0.628	0.596	0.665	0.279	0.402
LDAW2V	0.055	0.070	0.066	0.080	0.020	0.028
HDP	0.127	0.223	0.141	0.262	0.053	0.108

主题模型	AMI₁	AMI₂	NMI₁	NMI₂	ARI₁	ARI₂
R_GSDMM	0.441	$0 . 536$	0.450	$0 . 551$	0.315	$0 . 342$
GSDMM	0.421	0.484	0.433	0.501	0.260	0.274
BTM	0.426	0.503	0.460	0.546	0.176	0.274
LDA	0.201	0.343	0.209	0.357	0.103	0.192
LSA	0.338	0.387	0.418	0.443	0.107	0.181
LDAW2V	0.130	0.218	0.138	0.225	0.052	0.088
HDP	0.031	0.146	0.039	0.152	0.010	0.063

聚类数目	聚类方法	SC	DBI
	K-means++	$0 . 908$	0.546
DS₁	AGNES	0.906	$0 . 540$
	BIRCH	0.898	0.644
	GMM	0.847	0.720
	K-means++	$0 . 905$	$0 . 547$
DS₂	AGNES	0.903	0.587
	BIRCH	0.902	0.606
	GMM	0.840	0.816
	K-means++	$0 . 896$	$0 . 557$
DS₃	AGNES	0.891	0.579
	BIRCH	0.889	0.687
	GMM	0.794	1.087

数据集	聚类算法	AMI	NMI	ARI
	K-means++	0.443	0.465	0.274
DS₄	GMMBIRCH	0.4280.329	0.4600.373	0.2550.188
	AGNES	$0 . 445$	$0 . 467$	$0 . 277$
	K-means++	$0 . 688$	$0 . 704$	$0 . 520$
DS₅	GMMBIRCH	0.6780.496	0.7020.568	0.5220.279
	AGNES	0.687	0.703	0.519
	K-means++	$0 . 536$	$0 . 551$	$0 . 342$
DS₆	GMMBIRCH	0.5340.456	0.5410.485	0.3540.253
	AGNES	0.527	0.539	0.323

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数据集	标签	数量/条
DS₄	Internet of Things\Database\Analytics\Backend\News Services\Medical\Events\Entertainment\Location\Media	1 402
DS₅	Banking\Cloud\Music\Photos\Weather\Cryptocurrency\Stocks\Shipping\Bitcoin\Project Management	2 171
DS₆	Tools\Messaging\eCommerce\Science\Security\Telephony\Data\Transportation\Sports\Education\Travel\Video\Games\Adv ertising	5 751

Service clustering method based on description context feature words and improved GSDMM model

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数据集	标签	数量/条
DS₁	Financial\Tools\Messaging\Payments\eCommerce\Social\Mapping\Government\Data\Science\Security\Email\Telephony\Transpor- tation\ Reference\Enterprise	8 180
DS₂	DS1\Tools\Messaging\eCommerce\Science\SecurityData\Transportation\Sports\Education\Travel\Video\Advertising\Banking\Cloud\ Music\Photos\Weather\Cryptocurrency\Stocks\Shipping\Games\Telephony\	12 043
DS₃	all	18 439