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A clustering method for mashup services based on density peak detection

A clustering method and density peak technology, applied in the field of Mahsup service clustering based on density peak detection, can solve the problems of large amount of data, the clustering effect depends on the selection of the truncation distance, and the inability to pick out the cluster center point, etc. Reasonable density distribution, guaranteed accuracy and stability, and stable clustering effect

Active Publication Date: 2022-06-17
ZHEJIANG UNIV OF TECH
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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

Method used

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Embodiment Construction

[0042] The present invention will be further described below.

[0043] A method for clustering Mahsup service based on density peak detection. In the present invention, the feature vector of Mashup service can be a vectorized representation of feature information in Mahsup service by means of natural language processing technology or other feature information processing technology. In the Mashup service clustering scenario, this vector is the basic unit participating in the clustering, and each Mashup service feature vector is unique.

[0044] The service clustering method includes the following steps:

[0045] The first step is to calculate the local density, the distance between vectors and the closest distance of higher density for the feature vectors of all the Mashup services participating in the clustering; the process is as follows:

[0046] Step (1.1) Traverse each Mashup service feature vector and calculate the local density ρ of the current vector y , the calculati...

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Abstract

A kind of Mahsup service clustering method based on density peak detection, described method comprises the following steps: the first step, for all the characteristic vectors of the Mashup service that participates in clustering, carry out local density, distance between vectors and higher density shortest distance calculation ; The second step, based on the density information calculated in the first step, select the candidate points of the cluster centers from all the Mashup service feature vectors; the third step, further screen out the candidate points of the cluster centers obtained in the second step The most suitable K initial cluster centers are used for K-means clustering. The invention can effectively improve the clustering precision of Mashup services and reduce the service search space.

Description

technical field [0001] The invention relates to the field of Mashup service clustering, in particular to a Mahsup service clustering method based on density peak detection. Background technique [0002] Mashup technology is a convenient and efficient Web application development technology. It can quickly build a Mashup service that meets user needs by mixing and matching Web APIs with different functions. With the support of Mashup technology, software developers can usually refer to Mashup services with similar functions to complete the construction of Mashup services. However, with the rapid growth of the number and types of Mashup services on the Internet, how to quickly and accurately find the Mashup services that satisfy the most reference value from these massive service collections has become a challenging problem. [0003] A large amount of research work has shown that if the Mashup service is accurately clustered in advance, the search space of the service can be e...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/33G06F16/35G06F40/289G06F40/30G06K9/62
CPCG06F16/3344G06F16/35G06F18/23213
Inventor 陆佳炜吴涵马超治徐俊程振波肖刚
Owner ZHEJIANG UNIV OF TECH
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