Research Article
An Improved K-means Clustering Algorithm Over Data Accumulation in Delay Tolerant Mobile Sensor Network
@INPROCEEDINGS{10.1109/ChinaCom.2013.6694561, author={Yuhua Zhang and Kun Wang and Heng Lu and Huang Guo and Lili Xu}, title={An Improved K-means Clustering Algorithm Over Data Accumulation in Delay Tolerant Mobile Sensor Network}, proceedings={8th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2013}, month={11}, keywords={dt-msn lda dimensionality reduction k-means clustering analysis}, doi={10.1109/ChinaCom.2013.6694561} }- Yuhua Zhang
Kun Wang
Heng Lu
Huang Guo
Lili Xu
Year: 2013
An Improved K-means Clustering Algorithm Over Data Accumulation in Delay Tolerant Mobile Sensor Network
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2013.6694561
Abstract
Delay Tolerant Mobile Sensor Network (DT-MSN) possesses high delay tolerability so that the real-time requirement of data is reduced, which also results in data packet accumulation. The limitations of nodal buffer and great data accumulation have brought 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 of the dimension-reduced data. The 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.