NDLI: An improved k-means clustering algorithm over data accumulation in Delay Tolerant Mobile Sensor Network

Content Provider	IEEE Xplore Digital Library
Author	Yuhua Zhang Kun Wang Heng Lu Huang Guo Lili Xu
Copyright Year	2013
Description	Author affiliation: Key Lab. of Broadband Wireless Commun. & Sensor Network Technol., Nanjing Univ. of Posts & Telecommun., Nanjing, China (Yuhua Zhang; Kun Wang; Heng Lu; Huang Guo; Lili Xu)
Abstract	Delay Tolerant Mobile Sensor Network (DT-MSN) possesses high delay tolerability. And the real-time requirement of data is reduced, which results in data packet accumulation. The limitations of nodal buffer and great data accumulation have created data management problem. Thus, it seems particularly important to complete the task of quick analysis of the collected data in DT-MSN. To solve the problem of data accumulation, an improved k-means clustering algorithm is proposed based on linear discriminant analysis (LDA), namely LKM algorithm. In the algorithm, we firstly apply the dimension reduction method of LDA to change the high-dimension dataset into two dimensional dataset, then we use k-means algorithm for clustering analysis. Simulation results show that LKM algorithm shortens the sample feature extraction time, and improves the accuracy of k-means clustering algorithm, thus enhancing the performance of k-means clustering algorithm to analyze and process vast data.
Starting Page	34
Ending Page	39
File Size	563087
Page Count	6
File Format	PDF
ISBN	9781479914067
DOI	10.1109/ChinaCom.2013.6694561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Dimensionality reduction DT-MSN Clustering algorithms Scattering LDA Feature extraction Vectors Partitioning algorithms K-means clustering analysis Principal component analysis
Content Type	Text
Resource Type	Article

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