Detecting the fuzzy clusters of complex networks

Liu, Jian

doi:10.1016/j.patcog.2009.11.007

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…It should be remarked that, in contrast to the Kruse and Meyer's approach, statistical conclusions with Puri and Ralescu random fuzzy sets always concern the fuzzy-valued random element and the parameters associated with its induced distribution. An interesting distinctive feature of the statistical methodology based on this approach to generate fuzzy data is that most of the classical ideas in data analysis can be immediately preserved without needing to either define or adapt Huang and Ng (1999) and Lee and Pedrycz (2009) Functional data Tokushige et al (2007) and Tan et al (2013) Textual data (text data) Runkler and Bezdek (2003) Time data Coppi and D'Urso (2002, 2003, D'Urso (2005), Maharaj and D'Urso (2011, 2016, 2017b Spatial data Pham (2001) Spatial-time data Coppi et al (2010) and Disegna et al (2017) Three-way data Giordani (2010) and Rocci and Vichi (2005) Sequence data D'Urso and Massari (2013) Network data Liu (2010) Directional data Yang and Pan (1997) and Kesemen et al (2016) Distributional data Irpino et al (2017) Mixed data Yang et al (2004) Outlier data Davé (1991), Krishnapuram and Keller (1993), Frigui and Krishnapuram (1996), Wu and Yang (2002), D'Urso and Giordani (2006), Fritz et al (2013), Ferraro and Vichi (2015), Ferraro and Giordani (2017), D'Urso et al (2015aD'Urso et al ( , b, 2016D'Urso et al ( , 2017a, D'Urso and Leski (2016) and Yang and Nataliani (2017) Incomplete data …”

Section: On the Analysis And Classification Of Fuzzy Datamentioning

confidence: 99%

Fuzzy data analysis and classification

D’Urso

Alvarez

2017

Adv Data Anal Classif

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In analyzing and classifying data from a statistical perspective, fuzzy sets and logic have become a valuable tool either to model and handle imprecise data or to establish flexible techniques to deal with precise data.From the very beginning of his 52 years-old theory, Professor Zadeh highlighted that "Probability theory/statistics and fuzzy logic should be viewed as complementary rather than competitive," and he anticipated and encouraged the materialization of such a complementarity. Nowadays, this assertion is a reality, as shown by the many related papers, specialized conferences, special sessions and tracks in general conferences, and so on.This special issue started in 2015, with the 50th anniversary of the seminal paper on fuzzy sets by Zadeh (1965), aiming to collect a sample of research papers about the current trends on the combination of fuzzy sets/logic and data analysis/classification. When this special issue was almost ready for publication, Zadeh unfortunately passed away at age 96 (February 4, 1921-September 6, 2017. We wish this special issue to be dedicated to Professor Zadeh, as a modest part of the many tributes that he will receive, and intending to show that fuzzy sets/logic and data analysis/classification can certainly work in synergy.

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Section: On the Analysis And Classification Of Fuzzy Datamentioning

confidence: 99%

Fuzzy data analysis and classification

D’Urso

Alvarez

2017

Adv Data Anal Classif

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“…These methods assign the nodes to communities with different degree of belonging values and form overlapping communities. The most popular fuzzy center-based clustering model is fuzzy c-means (FCM) clustering and is mostly used in combination with other techniques for community detection (Jiang et al, 2009;Liu, 2010;Zhang et al, 2007). FCM clustering is the most well-known fuzzy clustering algorithm proposed by Dunn (1974) and extended by Bezdek (1981).…”

Section: Overlapping Community Detection Modelsmentioning

confidence: 99%

Fuzzy duocentric community detection model in social networks

2015

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a b s t r a c tThe main goal of this paper is to present a clustering model to identify duocentric communities in the complex networks. A duocentric community is built around two central nodes which are as close as possible to other nodes, while the central nodes are connected enough to each other to shape the center of the community. To detect such communities, we develop a new objective function based clustering model. The network's nodes are assigned to the duocentric communities by the type-2 fuzzy numbers which indicate the degrees of belonging to the communities by upper and lower membership values. Generated interval type-2 fuzzy membership values by our proposed model are able to determine how much each node belongs to the both central nodes and how it is shared among communities. Also, the compatible verification index with the proposed model is introduced to evaluate and compare the results of the proposed model with the existing approach in the literature. Finally, the performance of the proposed algorithm is validated by detecting duocentric communities in three artificial networks and two real social networks.

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“…Although some methods for discovering a fuzzy overlapping community have been presented recently, there is still space for improving their performance and universality . The most popular fuzzy clustering model is FCM and is mostly used in combination with other techniques for detecting communities . The structure of the fuzzy clustering model in these studies is not well adapted for graph clustering, specifically in the determination of the clusters’ centers.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Community Detection Model in Social Networks

Golsefid

Zarandi

Bastani

2015

Int. J. Intell. Syst.

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In this paper, a fuzzy graph clustering model is presented to identify overlapping communities in a complex network. A center‐based fuzzy clustering model is developed based on the possibilistic c‐means clustering model, and the distance measure is defined based on the similarity to the clusters’ centers. The performance of the clustering process is evaluated by intra and intercluster density. In addition, experimental results from two artificially generated networks and two real‐world networks (social interactions between karate club members and a part of the twitter network) indicate the new model's performance.

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Detecting the fuzzy clusters of complex networks

Cited by 15 publications

References 23 publications

Fuzzy data analysis and classification

Fuzzy data analysis and classification

Fuzzy duocentric community detection model in social networks

Fuzzy Community Detection Model in Social Networks

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