Density-based partitioning and clustering method for K center points in data mining

A clustering method and data mining technology, applied in the field of clustering, can solve problems that affect the clustering results, the local optimal solution of the results, and the sensitivity of the initial clustering center, so as to save computing time, stabilize the clustering results, and improve computing efficiency fast effect
CN104765879AInactive Publication Date: 2015-07-08无锡中科泛在信息技术研发中心有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
无锡中科泛在信息技术研发中心有限公司
Publication Date
2015-07-08
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to a density-based partitioning and clustering method for K center points in data mining. The method comprises the following steps that 1, a needed data set is given, and the clustering number K is determined; 2, the densities and average density of data objects are calculated; 3, the minimum density distance value of each data object in the data set is calculated; 4, the minimum density distance values of the data objects in the data set are descendingly sorted, and K data objects corresponding to the minimum density distance values are selected as a clustering center from large to small according to the determined clustering number K, wherein the densities of the K data objects are larger than the average density; 5, the data objects in the data set are distributed to an initial clustering center closest to the data objects, and a clustering result is obtained. The high-quality center points can be selected, subsequent iteration updating steps in a K-means algorithm are not needed, computation complexity is lowered, classification accuracy is improved, high stability is achieved, and operation efficiency is improved.
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Description

technical field

[0001] The invention relates to a clustering method, in particular to a density-based K center point division clustering method in data mining, which belongs to the technical field of clustering analysis. Background technique

[0002] Data mining is one of the hot topics in computer research today. As an unsupervised machine learning method, cluster analysis refers to how to automatically divide data objects into different clusters for a set of data objects, so that the same cluster Objects in a certain measure have high similarity, while data objects in different clusters have low similarity. Cluster analysis is widely used in cutting-edge fields such as machine learning, data mining, speech recognition, image segmentation, business analysis, and bioinformatics processing. At present, traditional clustering algorithms mainly include five categories, they are: partition-based clustering algorithms, hierarchical-based clustering algorithms, density-based clus...

Claims

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