International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 129

Research Paper | Computer Science & Engineering | China | Volume 5 Issue 4, April 2016


Clustering Algorithm Based on Local Random Walkwith Distance Measure

Gang Dai | Baomin Xu


Abstract: Cluster analysis is widely used in the field of data mining. However, the K-means algorithm which is widely used has a strong sensitivity for the initial values. Namely, the parameters such as clustering coefficient and centroid should be determined when the cluster is initialized. In the paper, we propose a K-means algorithm that based on link information and regard KL divergence distance as the objective function. This method not only introduces the way of the local random walk with the shortest path, but also uses the link information to convert the distance space. In other word, we utilize the local random walk with the shortest path to convert the distance between data into the transition probability of the random walk. Then, we use the random walk realize the conversion of the distance space. The core concept is the distance of converting node pair that refers to the node to the whole network node distance. The experimental results show that the proposed algorithm can improve the cluster result efficiently.


Keywords: Random walk, K-means, Clustering, KL divergence, Complex system


Edition: Volume 5 Issue 4, April 2016,


Pages: 337 - 341


How to Cite this Article?

Gang Dai, Baomin Xu, "Clustering Algorithm Based on Local Random Walkwith Distance Measure", International Journal of Science and Research (IJSR), Volume 5 Issue 4, April 2016, pp. 337-341, https://www.ijsr.net/get_abstract.php?paper_id=NOV162470

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