Augmenting Rapid Clustering Method for Social Network Analysis

Jayagopal, Prabhu; Sudharshan, M. Sujay; Saravanan, M.; Prasad, G. V. S. N. R. V.

doi:10.1109/asonam.2010.55

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…In this paper Prabhu et al, [7] propose an innovative clustering technique called the Rapid Clustering Method (RCM), which uses Subtractive Clustering combined with Fuzzy C-Means clustering along with a histogram sampling technique to provide quick and effective results for large sized datasets. Rapid Clustering Method can be used to cluster the dataset and analyze the characteristics in a social network.…”

Section: Related Workmentioning

confidence: 99%

Improved BSP Clustering Algorithm for Social Network Analysis

B¹

2011

BIJSESC

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Section: Related Workmentioning

confidence: 99%

Improved BSP Clustering Algorithm for Social Network Analysis

B¹

2011

BIJSESC

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“…The terminology of a cluster seems obvious: a group of data objects. The clusters found by different algorithms vary significantly in their properties, and understanding the cluster models is key to understanding the differences between the various algorithms [3][4][5].…”

Section: Clustering and Its Modelsmentioning

confidence: 99%

“…Relaxations of the complete connectivity requirement (a fraction of the edges can be missing) are known as quasicliques [4][5][6].…”

Section: Clustering and Its Modelsmentioning

confidence: 99%

“…With the help of RCM they can obtain rapid results and identify structural changes much more quickly. They considered the problem of clustering data over time and proposed a rapid clustering technique so that it can be used to generate quicker results for social network analysis [4].…”

Section: Related Workmentioning

confidence: 99%

“…As low precision searches are inevitable, tools must be provided to help users "cope" with (and make use of) these large document sets. Such tools should include means to easily browse through large sets of retrieved documents [1] and [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Phrase based Clustering Scheme of Suffix Tree Document Clustering Model

Jain¹,

Maheshwari²

2013

IJCA

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ABSTARCTDocument clustering is one of the difficult and recent research fields in the search engine research. Most of the existing documents clustering techniques use a group of keywords from each document to cluster the documents. Document clustering arises from information retrieval domains, and "It finds grouping for a set of documents belonging to the same cluster are similar and documents belongs to the different cluster are dissimilar". The nformation retrieval plays an important role in data mining for extracting the relevant information for related to user request. Information retrieval finds the file contents and identifies their similarity. It measures the performance of the documents by using the precision and recall. In this paper we proposed a phrase based clustering scheme which based on application of Suffix Tree Document Clustering (STDC) model. The proposed algorithm is designed to use the STDC model for accurate equivalent representation of document and similarity measurement of the similar documents. This method of clustering reduces the grouping time and similarity accuracy as compared to other existing methods.

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