Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014

Nayak, Janmenjoy; Naik, Bighnaraj; Behera, Himansu Sekhar

doi:10.1007/978-81-322-2208-8_14

Cited by 177 publications

(114 citation statements)

References 104 publications

Supporting

Mentioning

113

Contrasting

Order By: Relevance

“…The fuzzy logic theory is used so as to increase the mathematics ontology in a certain method with fuzziness in order to make an intelligent decision. We apply fuzzy c-means clustering (Nayak et al, 2015) on total requests, bad requests, bogus requests, unauthorized requests with respect to trust value to get the fuzzy sets and divide all requests into 25 clusters. These are the linguistic variable.…”

Section: Barr ¼ Bar Tr ð3þmentioning

confidence: 99%

Development of trust based access control models using fuzzy logic in cloud computing

Kesarwani

Khilar

2022

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

a b s t r a c tCloud computing is the technology that provides different types of services as a useful resource on the Internet. Resource trust value will help the cloud users to select the services of a cloud provider for processing and storing their essential information. Also, service providers can give access to users based on trust value to secure cloud resources from malicious users. In this paper, trust models are proposed, which comes under the subjective trust model based on the behavior of user and service provider to calculate the trust values. The trust is fuzzy, which motivated us to apply fuzzy logic for calculating the trust values of the cloud users and service providers in the cloud environment. We use a Mamdani fuzzy method with gauss membership function for fuzzification and triangular membership function for defuzzification. Parameters such as performance and elasticity are taken for trust evaluation of the resource. The attributes for calculating performance are workload and response time. And for calculating elasticity, we have taken scalability, availability, security, and usability. The fuzzy C-means clustering is applied to parameters for evaluating the trust value of users such as bad requests, bogus requests, unauthorized requests, and total requests.

show abstract

Section: Barr ¼ Bar Tr ð3þmentioning

confidence: 99%

Development of trust based access control models using fuzzy logic in cloud computing

Kesarwani

Khilar

2022

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

show abstract

“…An anomaly-detection approach based on graph data structure has been proposed by [16] for identifying anomaly users. In [17] an approach based on fuzzy techniques has been proposed for identifying an anomaly in unlabeled OSNs. Furthermore, graph-traversal queries have been used for profiling researchers over the DBLP dataset [18] while in [19,20] data quality techniques have been applied to longitudinal data.…”

Section: Related Workmentioning

confidence: 99%

A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

2019

View full text Add to dashboard Cite

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach's effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on.

show abstract

“…Fuzzy clustering, which is a type of overlapping clustering, differs from hard clustering. The FCM clustering algorithm assigns data points (examples) to a cluster, and the fuzzy membership of data points indicates the extent to which data points pertain to their clusters [31].…”

Section: Overlapping Clustering Algorithmmentioning

confidence: 99%

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Liwen

Liu

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC. Typically, existing cluster-based MLC frameworks employ a hard cluster method. In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class. Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm. Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable.

show abstract

Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014

Cited by 177 publications

References 104 publications

Development of trust based access control models using fuzzy logic in cloud computing

Development of trust based access control models using fuzzy logic in cloud computing

A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Contact Info

Product

Resources

About