Efficient subspace clustering for higher dimensional data using fuzzy entropy

Palanisamy, C.; Selvan, S.

doi:10.1007/s11518-009-5097-y

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…This algorithm was named as Entropy-Based Fuzzy Clustering (EFC). Palanisamy and Selvan (2009) developed an entropy-based fuzzy clustering method to identify relevant subspaces in the functional workspace. A heuristic method based on the Silhouette criterion was used to find the number of clusters.…”

Section: Literature Surveymentioning

confidence: 99%

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Dey

Pratihar

Datta

2011

Fuzzy Optim Decis Making

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A modified approach had been developed in this study by combining two well-known algorithms of clustering, namely fuzzy c-means algorithm and entropybased algorithm. Fuzzy c-means algorithm is one of the most popular algorithms for fuzzy clustering. It could yield compact clusters but might not be able to generate distinct clusters. On the other hand, entropy-based algorithm could obtain distinct clusters, which might not be compact. However, the clusters need to be both distinct as well as compact. The present paper proposes a modified approach of clustering by combining the above two algorithms. A genetic algorithm was utilized for tuning of all three clustering algorithms separately. The proposed approach was found to yield both distinct as well as compact clusters on two data sets.

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Section: Literature Surveymentioning

confidence: 99%

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Dey

Pratihar

Datta

2011

Fuzzy Optim Decis Making

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“…Fuzzy subsethood and entropy functions can be used in a variety of applications (image processing, feature selection, fuzzy classification, fuzzy controllers, fuzzy rules, similarity measures) and there are several presentations dealing with such measures (e.g., [7][8][9][10][11][12][13][14][15][16][17][18][19], to name a few after 2000). In [2], while examining the behavior of our measures, we saw that some of them had some interesting attributes which could offer additional information when specific applications are concerned.…”

Section: Introductionmentioning

confidence: 99%

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Bogiatzis

Papadopoulos

2019

Symmetry

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Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

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“…The extended version of Shannon entropy is fuzzy entropy, which is non-probabilistic entropy [25]. It adopts a new term named matchdegree to estimate entropy value [26] [27]. Match degree satisfies the four properties of de Luca-Termini axioms [25] [27].…”

Section: Introductionmentioning

confidence: 99%

“…It adopts a new term named matchdegree to estimate entropy value [26] [27]. Match degree satisfies the four properties of de Luca-Termini axioms [25] [27]. The probability of the entropy is computed using the number of occurring terms.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents

Revanasiddappa¹,

Harish²

2018

IJIMAI

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Selection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set.

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Efficient subspace clustering for higher dimensional data using fuzzy entropy

Cited by 15 publications

References 13 publications

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents

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