A Novel Spectral Clustering based on Local Distribution

Mandal, Jyotsna Kumar; Roy, Parthajit

doi:10.11591/ijece.v5i2.pp361-370

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Roy et al in [17] proposed a graph-based spectral clustering model, the method uses novel affinity matrix for spatial clustering with Mahalanobis distance, however, the method has some limitations, the distance metric can only measure from a single point, this reduces results quality.…”

Section: Related Workmentioning

confidence: 99%

Community detection of political blogs network based on structure-attribute graph clustering model

Al-Mukhtar¹,

Al-Shamery²

2019

IJECE

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Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.

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Section: Related Workmentioning

confidence: 99%

Community detection of political blogs network based on structure-attribute graph clustering model

Al-Mukhtar¹,

Al-Shamery²

2019

IJECE

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Scientific Documents clustering based on Text Summarization

Amoli¹,

Sh.

2015

IJECE

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<table border="1" cellspacing="0" cellpadding="0" width="593"><tbody><tr><td width="387" valign="top"><p>In this paper a novel method is proposed for scientific document clustering. The proposed method is a summarization-based hybrid algorithm which comprises a preprocessing phase. In the preprocessing phase unimportant words which are frequently used in the text are removed. This process reduces the amount of data for the clustering purpose. Furthermore frequent items cause overlapping between the clusters which leads to inefficiency of the cluster separation. After the preprocessing phase, Term Frequency/Inverse Document Frequency (TFIDF) is calculated for all words and stems over the document to score them in the document. Text summarization is performed then in the sentence level. Document clustering is finally done according to the scores of calculated TFIDF. The hybrid progress of the proposed scheme, from preprocessing phase to document clustering, gains a rapid and efficient clustering method which is evaluated by 400 English texts extracted from scientific databases of 11 different topics. The proposed method is compared with CSSA, SMTC and Max-Capture methods. The results demonstrate the proficiency of the proposed scheme in terms of computation time and efficiency using F-measure criterion.</p></td></tr></tbody></table>

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A Novel Spectral Clustering based on Local Distribution

Cited by 2 publications

References 23 publications

Community detection of political blogs network based on structure-attribute graph clustering model

Community detection of political blogs network based on structure-attribute graph clustering model

Scientific Documents clustering based on Text Summarization

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