Improved K-means service clustering method based on topic modeling

Abstract

The invention discloses an improved K-means service clustering method based on topic modeling. The improved K-means service clustering method comprises the following steps: 1, preprocessing Mashup service data needing feature representation; 2, based on the preprocessed Mashup service data, performing a function noun extraction operation; 3, for the function noun set FS of each Mashup service, utilizing a topic model to express a Mashup feature vector; 4, calculating density information of all Mashup feature vectors participating in clustering; step 5, based on the density information calculated in the step 5, screening out candidate points of the clustering center from all Mashup feature vectors; and step 6, for the clustering center candidate points obtained in the step 5, further screening out the most appropriate K initial clustering centers, and performing K-means clustering. According to the method, the final effect of Mahsup service clustering is improved.

Description

technical field [0001] The invention relates to the field of Mashup service data clustering in the Web environment, in particular to an improved K-means clustering method around topic modeling. 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 documents and related service attribute descript...

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

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