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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaoxu He Chenxi Shao Yan Xiong |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China (Xiaoxu He; Chenxi Shao; Yan Xiong) |
| 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. |
| Starting Page | 642 |
| Ending Page | 647 |
| File Size | 528478 |
| Page Count | 6 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781479951512 |
| DOI | 10.1109/ICNC.2014.6975911 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering methods Heuristic algorithms Force Clustering algorithms Nuclear Field Data Mining Vectors Field Force Data mining Dynamic Clustering Manganese |
| Content Type | Text |
| Resource Type | Article |
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