An Analysis of Fuzzy Clustering Methods

KumarMalhotra, Virender; Kaur, Harleen; Alam, M. Afshar

doi:10.5120/16497-6578

Cited by 14 publications

(20 citation statements)

References 19 publications

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“…At certain group level, dendrogram will break into another group level, thus producing a different data group. In hierarchical clustering, objects that belong to a child cluster also belong to the parent cluster [13].…”

Section: Methodsmentioning

confidence: 99%

“…The most well-known fuzzy clustering algorithms are: fuzzy c-means, fuzzy k-means, (ISODATA), Gustafson Kessel (GK) algorithm [13] etc.…”

Section: Methodsmentioning

confidence: 99%

“…The fuzzy c-means (FCM) algorithm involves the processes in which there is calculation of cluster centers and assignment of points to these centers using a formula of Euclidian distance [13]. The fuzzy c-means algorithm is one of the most widely used fuzzy clustering algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…This algorithm assigns a membership value to the data items for the clusters within a range of 0 to 1. Thus, the concepts of fuzzy sets of partial membership are incorporated and forms overlapping clusters for supporting it [13]. Consequently, the data objects closer to the centers of clusters have higher degrees of membership than objects scattered in the borders of clusters [20].…”

Section: Methodsmentioning

confidence: 99%

“…The theoretical basics on clustering methods, fuzzy clustering algorithms and their program software implementations are considered in numerous works [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. In this study, we used the fuzzy clustering approaches to identify disparities in the development of Ukrainian regions, which allow us to explore and utilize the advantages of this technique.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Analysis of regional development disparities in Ukraine with fuzzy clustering technique

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Disparities in the development of regions in any country affect the entire national economy. Detecting the disparities can help formulate the proper economic policies for each region by taking action against the factors that slow down the economic growth. This study was conducted with the aim of applying clustering methods to analyse regional disparities based on the economic development indicators of the regions of Ukraine. There were considered fuzzy clustering methods, which generalize partition clustering methods by allowing objects to be partially classified into more than one cluster. Fuzzy clustering technique was applied using R packages to the data sets with the statistic indicators concerned to the economic activities in all administrative regions of Ukraine in 2017. Sets of development indicators for different sectors of economic activity, such as industry, agriculture, construction and services, were reviewed and analysed. The study showed that the regional cluster classification results strongly depend on the input development indicators and the clustering technique used for this purpose. Consideration of different partitions into fuzzy clusters opens up new opportunities in developing recommendations on how to differentiate economic policies in order to achieve maximum growth for the regions and the entire country.

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Section: Methodsmentioning

confidence: 99%

“…The most well-known fuzzy clustering algorithms are: fuzzy c-means, fuzzy k-means, (ISODATA), Gustafson Kessel (GK) algorithm [13] etc.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of regional development disparities in Ukraine with fuzzy clustering technique

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Deep neural network and model-based clustering technique for forensic electronic mail author attribution

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2021

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Electronic mail is the primary source of different cyber scams. Identifying the author of electronic mail is essential. It forms significant documentary evidence in the field of digital forensics. This paper presents a model for email author identification (or) attribution by utilizing deep neural networks and model-based clustering techniques. It is perceived that stylometry features in the authorship identification have gained a lot of importance as it enhances the author attribution task's accuracy. The experiments were performed on a publicly available benchmark Enron dataset, considering many authors. The proposed model achieves an accuracy of 94% on five authors, 90% on ten authors, 86% on 25 authors and 75% on the entire dataset for the Deep Neural Network technique, which is a good measure of accuracy on a highly imbalanced data. The second cluster-based technique yielded an excellent 86% accuracy on the entire dataset, considering the authors' number based on their contribution to the aggregate data.

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An Analysis of Fuzzy Clustering Methods

Cited by 14 publications

References 19 publications

Analysis of regional development disparities in Ukraine with fuzzy clustering technique

Analysis of regional development disparities in Ukraine with fuzzy clustering technique

Empowering Hindu Women by Way of Smart Marriages

Deep neural network and model-based clustering technique for forensic electronic mail author attribution

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