Intuitionistic Fuzzy Clustering with Applications in Computer Vision

Iakovidis, Dimitris K.; Pelekis, Nikos; Kotsifakos, Evangelos E.; Kopanakis, Ioannis

doi:10.1007/978-3-540-88458-3_69

Cited by 45 publications

(17 citation statements)

References 18 publications

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“…In [5,11], Pelekis et al clustered IF representation of images and proposed a clustering approach based on the FCM using a novel similarity metric defined over IFSs, which is more noise tolerant and efficient as compared with the conventional FCM clustering of both crisp and fuzzy image representations.…”

Section: Related Workmentioning

confidence: 99%

An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

Visalakshi

Parvathavarthini

Thangavel

2013

Advances in Intelligent Systems and Computing

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Fuzzy c-means (FCM) clustering is one of the most widely used fuzzy clustering algorithms. However, the main disadvantage of this algorithm is its sensitivity to noise and outliers. Intuitionistic fuzzy set is a suitable tool to cope with imperfectly defined facts and data, as well as with imprecise knowledge. So far, there exists a little investigation on FCM algorithm for clustering intuitionistic fuzzy data. This paper focuses mainly on two aspects. Firstly, it proposes an intuitionistic fuzzy representation (IFR) scheme for numerical dataset and applies the modified FCM clustering for clustering intuitionistic fuzzy (IF) data and comparing results with that of crisp and fuzzy data. Secondly, in clustering of IF data, different IF similarity measures are studied and a comparative analysis is carried out on the results. The experiments are conducted for numerical datasets of UCI machine learning data repository.

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Section: Related Workmentioning

confidence: 99%

An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

Visalakshi

Parvathavarthini

Thangavel

2013

Advances in Intelligent Systems and Computing

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“…The ordinary fuzzy cpartition is the clustering result. An application of the proposed clustering technique to image segmentation was described in [14].…”

Section: Related Workmentioning

confidence: 99%

Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering

Viattchenin¹,

Shyrai²

2015

CAE

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This paper introduces a novel intuitionistic fuzzy set-based heuristic algorithm of possibilistic clustering. For the purpose, some remarks on the fuzzy approach to clustering are discussed and a brief review of intuitionistic fuzzy set-based clustering procedures is given, basic concepts of the intuitionistic fuzzy set theory and the intuitionistic fuzzy generalization of the heuristic approach to possibilistic clustering are considered, a general plan of the proposed clustering procedure is described in detail, two illustrative examples confirm good performance of the proposed algorithm, and some preliminary conclusions are formulated.

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“…In [20], [21], N. Pelekis et al investigated the issue of clustering intuitionistic fuzzy representation of images. For this, they proposed a clustering approach based on the FCM algorithm utilizing a novel similarity metric defined over IFS.…”

Section: Related Workmentioning

confidence: 99%

“…Intuitionistic Fuzzy Sets (IFS) [17] are generalized fuzzy sets that are useful in coping with the hesitancy originating from imperfect and imprecise information. Recently, limited attention has been paid in proposing intuitionistic fuzzy based clustering for centralized environment [18]- [21]. But, it is proved that intuitionistic fuzzy based FCM clustering can be more efficient and more effective than the well established FCM algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Approach to Distributed Fuzzy Clustering

Visalakshi¹,

Thangavel²,

Parvathi³

2010

IJCTE

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Abstract-Due to explosion in the number of autonomous data sources, there is an emergent need for effective approaches to distributed clustering. Intuitionistic Fuzzy Set is a suitable tool to cope with imperfectly defined facts and data, as well as with imprecise knowledge. This paper introduces a novel intuitionistic fuzzy based distributed clustering algorithm, to cluster distributed datasets, without necessarily downloading all the data into a single site. The process is carried out in two different levels: local level and global level. In local level, numerical datasets are converted into intuitionistic fuzzy data and they are clustered independently from each other using modified fuzzy C-Means algorithm. In global level, global centroid is computed by clustering all local cluster centroids. The global centroid is again transmitted to local sites to update the local cluster model. The new algorithm is compared against two existing ensemble based distributed clustering algorithms and centralized clustering where all the data are merged into a single data source and clustered. The simulated experiments described in this paper confirm good performance of the proposed algorithm.

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Intuitionistic Fuzzy Clustering with Applications in Computer Vision

Cited by 45 publications

References 18 publications

An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering

An Intuitionistic Fuzzy Approach to Distributed Fuzzy Clustering

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