Fast approximate K neighbor method based on tree strategy and balanced K-means clustering

A technology of K-means and K-nearest neighbors, which is applied to instruments, character and pattern recognition, computer components, etc., can solve the problems of low algorithm efficiency and achieve the elimination of uncertainty, reduction of search time, strong robustness and practicability Effect

CN110070121AActive Publication Date: 2019-07-30NORTHWESTERN POLYTECHNICAL UNIV
7 Cites 11 Cited by

Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2019-07-30

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention provides a fast approximate K neighbor method based on a tree strategy and balanced K-means clustering, so as to improve the performance and speed of approximate K neighbor retrieval. The method comprises the following steps: firstly, constructing a balanced K-means tree through a balanced K-means clustering method, enabling data to be organized in a tree-shaped structure efficientlyand orderly, and realizing rapid positioning of any new sample data; then, utilizing an anchor positioning method and a neighbor cluster idea, and quickly finding a plurality of approximate neighborpoints, namely K neighbor points, of the new data sample through a balance tree. According to the method, the advantages of a tree-based K-nearest neighbor algorithm and a balanced K-means algorithm are taken into account at the same time, and the method can be applied to the fields of image recognition, data compression, mode recognition and classification, machine learning, document retrieval systems, statistics, data analysis and the like.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention belongs to the technical field of machine learning and data mining, and in particular relates to a fast approximate K nearest neighbor method based on tree strategy and balanced K-means clustering. Background technique

[0002] In the era of mobile Internet, people's daily life is faced with the impact of massive data every day, such as personal information, video records, image collection, geographic information, log files, etc., in the face of such a large and growing data information, how to process all The effective storage, indexing and querying of the required information is a hot research topic both at home and abroad.

[0003] Approximate K-nearest neighbor retrieval was initially applied to document retrieval systems as a method for finding similar document information, and then in geographic information systems, K-nearest neighbor retrieval was also widely used in location information, query, analysis and statistics of spatial d...

Examples

Embodiment Construction

[0012] 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.

[0013] Such as figure 1 As shown, the present invention provides a fast approximate K-nearest neighbor method based on the tree strategy and balanced K-means clustering, which mainly consists of two main steps of building a balanced tree and finding K-nearest neighbors. The basic implementation process is as follows:

[0014] 1. Building a Balanced Tree

[0015] First, the input data set is clustered using the balanced K-means clustering algorithm, and the cluster centers of the two types of samples with equal sample numbers are obtained. Specifically:

[0016] The two types of balanced K-means clustering algorithm models are as follows:

[0017]

[0018] Among them, C is the center of the cluster, G is the index matrix, and X is the input data set, where the i-th row...