An online clustering algorithm

Author

Kan Li ; Fenglan Yao ; Ruipeng Liu

Author_Institution

Sch. of Comput., Beijing Inst. of Technol., Beijing, China

Volume

2

fYear

2011

fDate

26-28 July 2011

Firstpage

1104

Lastpage

1108

Abstract

This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation, adaptation, mergence, split and elimination. Experiments are carried out in three kinds of datasets to illustrate the performance of the SAFN algorithm for online clustering. Compared with SAKM algorithm, SAFN algorithm shows better performance in accuracy of clustering and multi-class high-dimension data.

Keywords

adaptive systems; learning (artificial intelligence); pattern clustering; SAFN learning procedure; SAKM algorithm; fast adaptive learning procedure; multi class environment; multi class high dimension data; nonstationary data; online clustering algorithm; Clustering algorithms; Gaussian distribution; Kernel; Neurons; Noise; Signal processing algorithms; Support vector machines; Online clustering; non-stationary data; self-adaptive feed-forward neural network; similarity measure;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-61284-180-9

Type

conf

DOI

10.1109/FSKD.2011.6019762

Filename

6019762