AB Distance Based Histogram Clustering for Mining Multi-Channel EEG Data Using WAVESIM Transform

摘要：

Temporal data mining is concerned with the analysis of temporal data and finding temporal patterns, regularities, trends, clusters in sets of temporal data In this paper we extract histogram features from the coefficients obtained by applying WaveSim Transform on Multi-Channel signals. WaveSim Transform is a reverse approach for generating Wavelet Transform like coefficients by using a conventional similarity measure between the function f(t) and the wavelet. We propose a method for histogram clustering based on AS distance measure which is based on the area and behavior difference components between the regression lines obtained from the histograms. The distance measure is used for k-means histogram clustering. WaveSim transform provides a means to analyze a temporal data at multiple resolutions and thus the clusters are obtained at multiple resolutions. The techniques have been tested on an EEG dataset recorded through 64 channels.

关键词： AB Distance Measure Regression Line Distance Histogram Clustering WaveSim Transform

作者: R. Pradeep Kumar P. Nagabhushan

作者单位: Department of Studies in Computer Science, University of Mysore, Kamataka, India - 570 006

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 467-477

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）