A new dynamic clustering method based on nuclear field

Abstract

Cluster analysis is an important and challenging subject in time series data mining. It has a very important application prospect in many areas, such as medical images, atmosphere, finance, etc. Many current clustering techniques have still many problems, for example, k-means is a very effective method in finding different shapes and tolerating noise, but its result severely depends on the suitable choice of parameters. Inspired by nuclear field in physics, we propose a new dynamic clustering method based on nuclear force and interaction. Basically, each data point in data space is considered as a material particle with a spherically symmetric field around it and the interaction of all data points forms a nuclear field. Through the interaction of nuclear force, the initial clusters are iteratively merged and a hierarchy of clusters are generated. Experimental results show that compared with the typical clustering method k-means, the proposed approach enjoys favorite clustering quality and requires no careful parameters tuning.