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Apparent density peak detection algorithm based on double-check

A peak detection and false density technology, which is applied in the field of computer computing, can solve problems such as increased error rate, insufficient flexibility, and poor results, and achieve the effects of improving accuracy, low error floor, and system bit error rate performance

Inactive Publication Date: 2016-04-13
HUAQIAO UNIVERSITY
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Problems solved by technology

However, due to the fixed scanning radius and insufficient flexibility of this type of algorithm, the density peak points found by this type of algorithm may have flaws and false peak points, that is, some points will be mistaken for peak points, so when dealing with some complex When the shape and distribution of data are disordered, the error rate of classification will increase. Therefore, dealing with these false peak points will help improve the accuracy of clustering results. Traditional density-based clustering algorithms, such as DBSCAN, etc., due to Its scanning radius is fixed, and the result is not good when dealing with complex structured data

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Embodiment Construction

[0011] The present invention will be further described below,

[0012] A false density peak detection algorithm based on double inspection, comprising the following steps:

[0013] 1) Calculate the distance matrix, input the data set P to be clustered, record the number of points as N, and record the i-th point in P as p i , calculate the distance between every two points in P, and generate a distance matrix (d i,j ) N×N , where d i,j represents the point p i to p j distance;

[0014] 2) Calculate the outer density of all points, using r 1 Scan each point as the outer radius, take the i-th point p i As an example, combine P with p i distance less than r 1 The point of adding to p i the r 1 -Neighbor collection 1Nei ι,1 ={p j |d i,j 1}middle. Will assemble Nei i,1 The number of elements in is taken as p i The outer density value of 1 ρ i , r 1 ...

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Abstract

The invention relates to an apparent density peak detection algorithm based on double-check. The apparent density peak detection algorithm based on double-check comprises the following steps that: a local density peak point is found out by calculating the distance between every two data concentration points to be clustered and outer densities of all points; and then, an apparent density peak point is finally found out by calculating the inner density of the local density peak point. Because of calculation of the inner density, the flexibility and the complex data processing capability of a density-based clustering algorithm are improved; the apparent density peak detection algorithm based on double-check can effectively increase the accuracy rate of the traditional density-based clustering algorithm and improve the bit error rate performance of the system; and thus, a relatively low error floor can be obtained.

Description

technical field [0001] The invention relates to the field of computer operation, in particular to a false density peak detection algorithm based on double inspection. Background technique [0002] Clustering is a process of classifying and distinguishing physical or abstract objects according to their similarities. For decades, clustering has been one of the important research contents in data mining, pattern recognition and other research directions. Traditional density-based clustering algorithms, such as DBSCAN, usually have better clustering results. However, due to the fixed scanning radius and insufficient flexibility of this type of algorithm, the density peak points found by this type of algorithm may have flaws and false peak points, that is, some points will be mistaken for peak points, so when dealing with some complex When the shape and distribution of data are disordered, the error rate of classification will increase. Therefore, dealing with these false peak ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/00G06F2218/12G06F18/23
Inventor 陈叶旺汤盛宇王成
Owner HUAQIAO UNIVERSITY
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