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Spectral Clustering with Sparse Graph Construction Based on Markov Random Walk
( Jiangzhong Cao ),( Pei Chen ),( Bingo Wing-kuen Ling ),( Zhijing Yang ),( Qingyun Dai ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7
Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.
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Zhao, Chuanxin,Wu, Changzhi,Wang, Xiangyu,Ling, Bingo Wing-Kuen,Teo, Kok Lay,Lee, Jae-Myung,Jung, Kwang-Hyo Butterworths [etc.] 2017 Applied mathematical modelling Vol.49 No.-
<P><B>Abstract</B></P> <P>Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment significantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer-based diffusion particle swarm optimization method is proposed to solve this large-scaled optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Furthermore, our method has good scalability which can be used to deploy a large-scaled sensor network.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Model jointly optimal sink placement and sensor-to-sink routing as a mixed integer optimization problem. </LI> <LI> Propose a discrete particle swarm optimization for sensor-to-sink routing. </LI> <LI> Develop a novel diffusion particle swarm optimization for sinks placement. </LI> <LI> Show superiority of our method over existing results through experimental comparisons. </LI> </UL> </P>
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