Data Mining Through Fuzzy Social Network Analysis

Nair, Premchand S.; Sarasamma, S.T.

doi:10.1109/nafips.2007.383846

Cited by 40 publications

(21 citation statements)

References 6 publications

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“…Their proposed approach successfully identified and analyzed a selected set of hate groups (which contained 820 bloggers) on Xanga. Nair and Sarasamma [85] performed SNA alongside fuzzy theory with the aim of modeling multi-modal social networks. A new fuzzy binary operation was proposed to satisfy the requirements of a fuzzy consolidation operator.…”

Section: Social Network Analysis (Sna)mentioning

confidence: 99%

A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others

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Section: Social Network Analysis (Sna)mentioning

confidence: 99%

A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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“…[39][40][41][42][43][44][45][46] applied fuzzy sets [36] for representing the social networks and analyzing the network. Commonly, a social network with vague relationships is represented with a fuzzy graph, so called fuzzy social networks [47].…”

Section: Fuzzy Logic Based Representation Approachmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

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“…Fan et al [25,26] discuss structural equivalence and regular equivalence in fuzzy social networks. Data mining through fuzzy social network analysis are discussed by Premchand and Suseela [27].…”

Section: Introductionmentioning

confidence: 99%

The Closeness Centrality Analysis of Fuzzy Social Network Based on Inversely Attenuation Factor

Zhang

Liao

2013

Fuzzy Information &Amp; Engineering and Operations Research &Amp; Management

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Fuzzy centrality analysis is one of the most important and commonly used tools in fuzzy social network. This is a measurement concept concerning an actor's central position in the fuzzy social network, and it reflects the different positions and advantages between social network actors. In this paper we extend the notion of centrality and centralization to the fuzzy framework, propose fuzzy inversely attenuation closeness centrality, and discussed fuzzy group closeness centralization based on inversely attenuation factor in fuzzy social networks.

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Data Mining Through Fuzzy Social Network Analysis

Cited by 40 publications

References 6 publications

A review of data mining applications in crime

A review of data mining applications in crime

When social computing meets soft computing: opportunities and insights

The Closeness Centrality Analysis of Fuzzy Social Network Based on Inversely Attenuation Factor

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