NDLI: UCK-means :A customized K-means for clustering uncertain measurement data

Content Provider	IEEE Xplore Digital Library
Author	Peng Yu Luo Qinghua Peng Xiyuan
Copyright Year	2011
Description	Author affiliation: Automatic Test and Control Institute, Harbin Institute of Technology, HIT, China (Peng Yu; Luo Qinghua; Peng Xiyuan)
Abstract	Due to some reasons such as transmitting error or outdated or imprecise measurement, data uncertainty is an inherent property in wireless sensor networks or in LXI test framework. When we apply data mining techniques to these uncertain data, we must consider the uncertainty to get better data mining results. At present, most of uncertain data clustering methods assume the probability density functions or probability distribution function of whole data is available. However, in many real applications, this piece of information is rarely available. Only limited uncertain information may be available, such as the standard deviation. In this paper, we adopt a more realistic assumption that the standard deviation of individual measurement data is available, and propose a new uncertain distance computing method between multi-dimensional uncertain data. In addition, we propose an uncertain customized data clustering algorithm based on the classical K-means to process the multi-dimensional uncertain data. Experiment results show that the uncertain clustering algorithm can produce better results with lower complexity.
Starting Page	1196
Ending Page	1200
File Size	315046
Page Count	5
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6019639
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature measurement Iris Uncertainty Accuracy Correlation Wireless Sensor Network uncertain data Clustering algorithms data mining data clustering Data mining
Content Type	Text
Resource Type	Article

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