A fast adaptive nearest neighbor clustering method based on structured anchor graph

A clustering method and self-adaptive technology, applied to instruments, character and pattern recognition, computer components, etc., can solve problems such as joint optimization difficulties, unstable clustering performance, and algorithms without effective learning data, etc., to reduce computational complexity Degree, the effect of good clustering results

Inactive Publication Date: 2019-03-22
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, the clustering results of these algorithms seem to be suboptimal, most likely because these algorithms are not designed to effectively learn data relationships
On the one hand, the performance of spectral analysis is limited by the quality of the similarity matrix
On the other hand, the so...

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  • A fast adaptive nearest neighbor clustering method based on structured anchor graph
  • A fast adaptive nearest neighbor clustering method based on structured anchor graph
  • A fast adaptive nearest neighbor clustering method based on structured anchor graph

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, and the present invention includes but not limited to the following embodiments.

[0025] Such as figure 1 As shown, the present invention provides a fast adaptive nearest neighbor clustering method based on a structured anchor graph, and its basic implementation process is as follows:

[0026] 1. Generate representative anchor points.

[0027] In order to reduce the time complexity required for clustering calculations, it is necessary to reduce the data size as much as possible while maintaining the original data structure. Input original data matrix X=[x 1 ,...,x n ] T , use the K-means algorithm to generate m representative anchor points from n original data points, and get the anchor point matrix U=[u 1 ,...,u m ] T , where x i is the i-th original data point, a 1×d-dimensional vector, i=1,...,n, n is the number of original data points, u j i...

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Abstract

The invention provides a fast adaptive nearest neighbor clustering method based on a structured anchor graph. First of all, using K-Means algorithm generates representative anchors; Then, the initialsimilarity matrix is constructed for the original data points and anchor points. Then, the similarity matrix is updated iteratively by using the structured anchor graph technique, and the nearest neighbor assignment is performed adaptively. Finally, the clustering results are obtained directly according to the connected components of the graph corresponding to the final similarity matrix. The method of the invention reduces the dependence of the large-scale spectral clustering task on the initial similarity matrix weight, and can quickly obtain high-quality clustering results by iteratively optimizing the anchor graph structure.

Description

technical field [0001] The invention belongs to the technical field of machine learning and data mining, and in particular relates to a fast self-adaptive nearest neighbor clustering method based on a structured anchor graph. Background technique [0002] With the explosive growth of smart devices and the popularity of the Internet and the Internet of Things, people's behavior data is being collected anytime and anywhere. As one of the most widely used techniques in exploratory data analysis, clustering, an unsupervised data learning method, is being increasingly applied to the preprocessing of large amounts of unlabeled data by academia and industry. From statistics, computer science, biology all the way to social science or psychology. In almost every scientific field that involves empirical data processing, people try to gain their intuition about the data by identifying combinations of "similar behavior" in the data. [0003] Spectral clustering algorithm is one of the...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/23
Inventor 聂飞平王成龙王宏王榕于为中李学龙
Owner NORTHWESTERN POLYTECHNICAL UNIV
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