Potential energy cluster algorithm for automatically determining cluster center

Abstract

The present invention discloses a potential energy cluster algorithm for automatically determining a cluster center. The objective of the invention is mainly to solve the two defects that the potential-based hierarchical agglomerative clustering (PHA) needs to artificially arrange the number and the distribution mechanism only depends on the distance so as to weaken the potential energy influence. The method comprises: obtaining the potential energy of each data point, arranging the potential energy of each data point from big to small, finding out the distance from father nodes of each data point to the father nodes, calculating the [Gamma]i value of each node, employing the K-means algorithm to perform clustering (K is 2) in the one-dimensional space according to the [Gamma]i value of each node, and selecting one class with few data points as a clustering center set. After the clustering center is determined, each cluster center represents one class, and the residual data points are included into the cluster where the samples with having the smaller potential energy and having the nearest distance are located. The potential energy cluster algorithm for automatically determining the cluster center automatically determines the number of the clusters and has high accuracy and higher practicality.

Description

technical field [0001] The invention belongs to the technical field of cluster analysis, and relates to the improvement and optimization of a potential-based fast hierarchical clustering algorithm (Potential-based hierarchical agglomerative clustering, PHA). Specifically, it is a potential energy clustering algorithm for automatically determining cluster centers, which can be used in pattern recognition, data mining, image processing and other fields. Background technique [0002] Clustering algorithm is an important branch of data mining, which gathers data through some similarity measure and clustering criterion without prior knowledge about potential data distribution. Cluster analysis plays an important role in many fields, including biology, artificial intelligence, customer relationship management, information retrieval, machine learning, etc. [0003] However, there are many widely used clustering algorithms that cannot automatically determine the number of clusters....

AI Technical Summary

Problems solved by technology

[0006] In view of the problems existing in the PHA algorithm in the above-mentioned background technology, the present invention proposes a potential energy clustering algorithm that automatically determines the clustering center: ACP (Automatically Clustering based-on Potential metric), to solve the problem that the PHA algorithm needs to manually set the number of clusters and The allocation mechanism does not consider comprehensive issues

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