A study on possibilistic and fuzzy possibilistic C-means clustering algorithms for data clustering

Jafar, O.A. Mohamed; Sivakumar, R.

doi:10.1109/incoset.2012.6513887

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…The PC indicates the average relative total of membership sharing among pairs of fuzzy subsets [21], the values rang is [ , 1] ( is the number of clusters) where a high PC score indicates a better partitioning. The PE reveals the repartition of entities within the clusters [22], the values rang is [0, ], where a low score of PE indicates a better quality of partitioning. Purity of a cluster is the fraction of the highest number of elements shared with other clusters to the total number of elements in the cluster.…”

Section: A Clustering Evaluationmentioning

confidence: 99%

Ontology Knowledge Mining for Ontology Alignment

Idoudi¹,

Ettabaa²,

Solaiman³

et al. 2016

IJCIS

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As the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies.This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously. Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval. Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques. Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The proposed approach has been tested over the OAEI benchmark dataset and some real mammographic ontologies since this work is a part of CMCU project for Mammographic images analysis for Assistance Diagnostic Breast Cancer. The system performs good results in the terms of precision and recall with respect to other alignment system.

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Section: A Clustering Evaluationmentioning

confidence: 99%

Ontology Knowledge Mining for Ontology Alignment

Idoudi¹,

Ettabaa²,

Solaiman³

et al. 2016

IJCIS

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“…Memberships and typicalities are significant for the accurate characteristic of data substructure in clustering problem (Jafar and Sivakumar 2012).…”

Section: Introductıonmentioning

confidence: 99%

Ecoli Veri Protein Lokalizasyonunda Bulanık ve Olabilirlikli Kümeleme Algoritmalarının Analizi

Özdemir

Kaya²

2019

Afyon Kocatepe University Journal of Sciences and Engineering

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Clustering is a process of dividing the objects into subgroups so that the same set of data is similar, but the data of different clusters is different. The basis of the fuzzy clustering algorithms is the C-Means families and the strongest algorithm is the Fuzzy C-means (FCM) algorithm. In this study; FCM, Possibilistic Fuzzy C-means (PFCM), Fuzzy Possibilistic C-means (FPCM) and Possibilistic C-means (PCM) algorithms are used to classify the several real data sets which are E.coli, wine and seed data sets into different clusters by MATLAB program. Also, the results of PFCM, FPCM, PCM and FCM algorithms are compared according to the classification accuracy, root mean squared error (RMSE) and mean absolute error (MAE). The results show that the PFCM and FPCM algorithms have better performance than FCM and PCM according to criteria for comparing the performances.

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