On the Global Features and Nuclear Field Clustering of Long Time Series

• 1、College of Computer Science and Technology, USTC

Abstract：Cluster analysis is an important and challenging subject in time series data mining. It has a very important application prospects in many areas, such as medical images, atmosphere, finance, etc. Many current clustering techniques still have 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 field and global features for long time series. Basically, each time series is mapped to one data point in global features space, then the data point 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 is 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.

keywords： Dynamic Clustering Data Mining Global Features Nuclear Field Field Force

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