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  Formation of Metagraph Using Clustering  
  Authors : Seema Gaur; Praveen Dhyani
  Cite as:

 

A cluster is grouping of similar objects. In clustering we find object that must be sufficiently close (or similar) to one another. In this paper we deal with a new graph structure called metagraph, which show meta-node to meta-node mapping. This paper explains the clustering methods, based on metagraph clustering. The metagraph clustering method based on the concept that intra-cluster and inter-cluster similarity. We use concept of clustering to construct a metagraph. Clustering metagraph is a natural way for handling different values.

 

Published In : IJCSN Journal Volume 3, Issue 5

Date of Publication : October 2014

Pages : 402 - 404

Figures : 01

Tables : --

Publication Link : Formation of Metagraph Using Clustering

 

 

 

Seema Gaur : received the M.Tech degrees in Computer Science Engineering from Birla Institute of Technology Mesra Ranchi in 1999 .Working as Assistant Professor(Computer Science ) in BIT jaipur campus .

 

 

 

 

 

 

 

Metagraph

Cluster

Meta-node

Metagraph allows different components of the process to be represented both graphically and analytically. Considering an example which show a metagraph which is constructed in matlab using some function of matlab . Using the same we also construct a metagraph for social data clustering and make a social metagraph.

 

 

 

 

 

 

 

 

 

[1] Alsaleh Slah, Nayak Richi and Xu Yue, Grouping People in Social Networks Using a Weighted Multi- Constraints Clustering Method, WCCI 2012 IEEE World Congress on Computational Intelligence . 2012. [2] Basu A. and Blanning R. W. ,Metagraph , Omega International Journal of Management Sciences, Vol.23, 1995, pp. 13-25. [3] C.T Zahn(1971) : Graph-theoretical methods for detecting and describing clusters , IEEE transaction on Computers c(20):68-86. [4] Gaur D., Shastri A. and Biswas R. , Metagraph: A new model of data structure ICCSIT IEEE Computer Society Singapore, 2008, pp.729-733. [5] Lahiri and T. Berger-Wolf, Mining Periodic Behavior in Dynamic Social Networks,Proceedings of the 8th IEEE Inter national Conference on Data Mining, 2008, pp. 37382. [6] M.E.J. Newman and M. Girvan, Finding and Evaluating Community Structure in Networks,Physical Review E, vol. 69, no. 2, 2004, pp. 026113-15. [7]. M.E.J. Newman, Fast Algorithm for Detecting Community Structure in Networks,Physical Review E, vol. 69, 2004, pp. 066133-1. [8] Thair Nu Phyu, Survey of Classification Techniques in DataMining IMECS 2009, Hong Kong.