Web data clustering method based on Mashup service function feature representation and density peak detection

Abstract

The invention discloses a clustering method based on Mashup service function feature representation and density peak detection. The clustering method comprises the following steps: 1, preprocessing all Mashup service data needing feature representation; 2, carrying out function noun extraction operation; 3, performing semantic association calculation on the semantic weight of each functional noun;4, in combination with a TF-IDF algorithm and a Word2Vec model, performing expression of Mashup semantic feature vectors; 5, calculating density information of all Mashup semantic feature vectors participating in clustering; 6, screening out candidate points of the clustering center from all the Mashup semantic feature vectors; and 7, further screening out the most suitable K initial clustering centers, and carrying out K-means clustering. According to the method, the functional characteristics of the Mashup service can be effectively expressed, and the clustering performance of the Mashup service is enhanced.

Description

technical field [0001] The invention relates to the field of Mashup service data clustering in the Web environment, in particular to a clustering method based on Mashup service function feature representation and density peak detection. Background technique [0002] As one of the core technologies of the Web 2.0 era, Mashup technology can realize the integration of heterogeneous resources by combining WebAPI services with different functions. Once this convenient and efficient development technology came out, it has been favored by the majority of software developers, and many organizations have also released their own developed Mashup services and data resources to the Internet for users to call. However, with the continuous growth of mashup service resources on the Internet, how to help users quickly locate mashup services that meet their own needs has become an urgent problem to be solved. In addition, because most of the current Mashup services lack normative WSDL docum...

AI Technical Summary

Problems solved by technology

Although the idea of ​​the DPC algorithm is concise and efficient, there are still some problems in practical applications: (1) the clustering effect is very dependent on the selection of the cut-off distance; (2) when the amount of data is large, it may not be easy to pick out Appropriate cluster center point

Images