Clustering based on words distances

In order to find the relevance of the key words in the hot topics effectively, we proposed a clustering method based on words-distances. We calculated the distances between the words firstly, then calculated the sectional density of each words. We regarded the words which have higher sectional density and far away from sectional density point as the center point in the clustering. After find the center point, we start to clustering. This method through decision diagram on estimating the number of clusters. At last, we can find the results on the evaluating indicator of accuracy rate and recall rate.

[1]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[2]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[3]  Santo Fortunato,et al.  Finding Statistically Significant Communities in Networks , 2010, PloS one.

[4]  He Hui,et al.  The Research on Term Field Based Term Co-Occurrence Model , 2007, Third International Conference on Semantics, Knowledge and Grid (SKG 2007).

[5]  Ronghua Shang,et al.  Community detection based on modularity and an improved genetic algorithm , 2013 .

[6]  Bin Wu,et al.  Research and Evaluation on Modularity Modeling in Community Detecting of Complex Network Based on Information Entropy , 2009, 2009 Third IEEE International Conference on Secure Software Integration and Reliability Improvement.

[7]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[8]  Xueqi Cheng,et al.  Spectral methods for the detection of network community structure: a comparative analysis , 2010, ArXiv.

[9]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[10]  Mark E. J. Newman,et al.  An efficient and principled method for detecting communities in networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[12]  Youngdo Kim,et al.  Map equation for link communities. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Steve Gregory,et al.  Finding overlapping communities in networks by label propagation , 2009, ArXiv.

[14]  Jianfeng Gao,et al.  Resolving query translation ambiguity using a decaying co-occurrence model and syntactic dependence relations , 2002, SIGIR '02.

[15]  Shen Hua,et al.  Information Bottleneck Based Community Detection in Network: Information Bottleneck Based Community Detection in Network , 2009 .

[16]  Huaiyu Wan,et al.  Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks , 2012, Journal of Computer Science and Technology.

[17]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[18]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[19]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[20]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Xie Fei Spam Filter Based on Term Co-Occurrence Model , 2009 .

[22]  Dino Pedreschi,et al.  DEMON: a local-first discovery method for overlapping communities , 2012, KDD.

[23]  Marko Bajec,et al.  Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Steven P Gross,et al.  Modularity optimization by conformational space annealing. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[26]  Andreas W. M. Dress,et al.  A spectral clustering-based framework for detecting community structures in complex networks , 2009, Appl. Math. Lett..

[27]  Haiqiang Chen,et al.  Information Bottleneck Based Community Detection in Network: Information Bottleneck Based Community Detection in Network , 2009 .

[28]  Boleslaw K. Szymanski,et al.  Towards Linear Time Overlapping Community Detection in Social Networks , 2012, PAKDD.

[29]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.