Density peaks clustering method optimized by K nearest neighbor's similarity

A technology of density peak aggregation and similarity, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of incorrect processing of manifold data clustering, and achieve the effect of wide applicability

Inactive Publication Date: 2017-11-24
JIANGNAN UNIV
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Problems solved by technology

[0004] In view of the above problems, the present invention proposes a Density Peaksclustering Optimized by K Nearest Neighbor's Similarity (DPCKS) method, which can solve the problem that the original density peak clustering algorithm cannot correctly process manifold data clustering. problem, which improves the scope of application of the algorithm and can meet the needs of practical engineering applications

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  • Density peaks clustering method optimized by K nearest neighbor's similarity
  • Density peaks clustering method optimized by K nearest neighbor's similarity
  • Density peaks clustering method optimized by K nearest neighbor's similarity

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[0029] 1. Introduction to basic theory

[0030] 1. Density peak clustering algorithm

[0031] Density Peak Clustering Algorithm DPC is a density-based clustering algorithm that can automatically discover the number of groups and assign them. The algorithm computes the local density ρ for each point i i and the distance δ to the nearest point with a density greater than it i . where the local density ρ i It is defined as follows:

[0032]

[0033]

[0034] Among them, d ij is the Euclidean distance between sample points i and j, d c To cut off the distance, usually after sorting the distances between all points from small to large, take the distance that is 2% to 5% small.

[0035] δ i is the shortest distance from sample point i to point j with higher local density, which is calculated as

[0036]

[0037] For the point with the largest global density, let δ i =max j d ij .

[0038] by ρ i The calculation shows that the local density of the sample is aff...

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Abstract

The present invention discloses a density peaks clustering method optimized by K nearest neighbor's similarity (DPCKS). The problem is mainly solved that the density peaks clustering (DPC) cannot process manifold data clustering. The method employs a new function to calculate inter-point similarity and search K nearest neighbor of each point, and employs the K nearest neighbor to perform pointing point detection when distribution of each point to renewedly search its pointing point about a point with a wrong pointing and finally distribute residual points to a clustering where the pointing point is located. The method provided by the invention can be suitable for manifold data clustering, has higher precision and application range and can satisfy the demand of actual engineering application.

Description

technical field [0001] The invention belongs to the fields of data mining and intelligent information processing, and relates to manifold data clustering processing; specifically, it relates to a K-nearest neighbor similarity-optimized density peak clustering method, which can be used in the fields of data mining, pattern recognition, machine learning and the like. Background technique [0002] Clustering refers to the analytical process of grouping a collection of physical or abstract objects into classes consisting of similar objects. It is an important human behavior. The purpose of clustering is simply to classify similar data. Clustering has roots in many fields, including mathematics, computer science, statistics, biology, and economics. Clustering has been widely researched and applied in data mining, pattern recognition, machine learning, information retrieval and other fields. Clustering is an exploratory analysis. In the process of classification, people do not ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23211
Inventor 葛洪伟朱庆峰江明李莉
Owner JIANGNAN UNIV
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