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Journal Article

Enforcement of Rough Fuzzy Clustering Based on Correlation Analysis

TL;DR: This paper proposes Fuzzy to Rough FuzzY Link Element (FRFLE) which is used as an important factor to conceptualize the rough fuzzy clustering from the fuzzy clusters result and shows that proposed RFCM algorithm using FRFLE deals with less computation time than the traditional RFCM algorithms.
Abstract: Clustering is a standard approach in analysis of data and construction of separated similar groups. The most widely used robust soft clustering methods are fuzzy, rough and rough fuzzy clustering. The prominent feature of soft clustering leads to combine the rough and fuzzy sets. The Rough Fuzzy C-Means (RFCM) includes the lower and boundary estimation of rough sets, and fuzzy membership of fuzzy sets into c-means algorithm, the widespread RFCM needs more computation. To avoid this, this paper proposes Fuzzy to Rough Fuzzy Link Element (FRFLE) which is used as an important factor to conceptualize the rough fuzzy clustering from the fuzzy clustering result. Experiments with synthetic, standard and the different benchmark dataset shows the automation process of the FRFLE value, then the comparison between the results of general RFCM and RFCM using FRFLE is observed. Moreover, the performance analysis result shows that proposed RFCM algorithm using FRFLE deals with less computation time than the traditional RFCM algorithms.
Citations
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Proceedings ArticleDOI
22 Mar 2018
TL;DR: An approach based on the combination of Kapur's entropy and K-means clustering is considered here to mine the optic disc region from the RGB retinal picture to assess the Retinal-Optic-Disc to assess its condition.
Abstract: Generally, retinal picture valuation is commonly executed to appraise the diseases. In this paper, an image examination technique is implemented to extract the Retinal-Optic-Disc (ROD) to assess its condition. An approach based on the combination of Kapur's entropy and K-means clustering is considered here to mine the optic disc region from the RGB retinal picture. During the experimental implementation, this approach is tested with the DRIVE and RIM-ONE databases. Initially, the DRIVE pictures are considered to appraise the proposed approach and later, the RIM-ONE dataset is considered for the testing. After extracting the ROD, comparative analyses with the expert's Ground-Truths are carried out and the image similarity values are then recorded. This approach is then validated against the Otsu's+levelset existing in the literature. All these experiments are implemented using Matlab2010. The outcome of this procedure confirms that, proposed work provides better picture similarity values compared to Otsu's+levelset. Hence, in future, this procedure can be considered to evaluate the clinical retinal images.

8 citations


Cites background from "Enforcement of Rough Fuzzy Clusteri..."

  • ..., Gk) for (k ≤ n) in order to reduce the within-cluster sum of squares [24-28]....

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Proceedings ArticleDOI
01 Jun 2020
TL;DR: The extraction of water resources from nonwater bodies, for example, vegetation, urban regions, and so forth, is exhibited using machine learning (ML) algorithms, using data collected from BHUVAN open data archive.
Abstract: Perception of surface water is a utilitarian necessity for contemplating natural and hydrological processes. Ongoing advances in satellite-based optical remote sensors have advanced the field of detecting surface water to another period. Observing surface water with old-style strategies isn't a simple undertaking. Remote detecting with wide inclusion and different fleeting observing is the best answer for surface water checking, This paper exhibits the extraction of water resources from nonwater bodies, for example, vegetation, urban regions, and so forth. Using machine learning (ML) algorithms. The data used in the process have been collected from BHUVAN open data archive. This paper also targets measuring the area of a particular water body using GIS. Water bodies have strong absorbability and low radiation in the range from visible to infrared wavelength. CNN speaks of a blueprint for all-round picture handling using neural means. CNN force imperative casing function admirably fit the preparation of spatially or momentarily coursed data. The results display the binary classified output which has been extracted using a neural network and also waterbody statistics using GIS

1 citations


Cites background from "Enforcement of Rough Fuzzy Clusteri..."

  • ...The suggested procedure is astoundingly essential and depends just upon the hypothesis that the water constituent in the picture is a gentle domain [8]....

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Journal ArticleDOI
TL;DR: This work provides alternative clustering algorithms that have been applied to the same dataset and yielded diverse clustering outcomes and chooses the most appropriate one to handle the situation at hand.
Abstract: In clustering problem analysis, Ensemble Cluster is proven to be a viable solution. Creating a cluster for such a comparable dataset and combining it into a separate grouping the clustering quality may be improved by using the combining clustering technique. Consensus clustering is another term for Ensemble clustering. Cluster Ensemble is a potential technique for clustering heterogeneous or multisource data. The findings of spectral ensemble clustering were utilized to reduce the algorithm's complexity. We now provide alternative clustering algorithms that have been applied to the same dataset and yielded diverse clustering outcomes. Because the many strategies were all described, it was easier to choose the most appropriate one to handle the situation at hand. To forecast the degree of student achievement in placement, clustering is created on the preprocessed information using clustering's specifically normalized k-means comparing with K-Medoids and Clarans algorithms.

1 citations

References
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Journal ArticleDOI
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

5,744 citations


"Enforcement of Rough Fuzzy Clusteri..." refers methods in this paper

  • ...The clustering approach [1, 2, 22] can be classified into two classifications such as soft and hard clustering....

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01 Jan 2007

4,221 citations


"Enforcement of Rough Fuzzy Clusteri..." refers methods in this paper

  • ...The clustering approach [1, 2, 22] can be classified into two classifications such as soft and hard clustering....

    [...]

Journal ArticleDOI
TL;DR: The fundamental concepts of cluster validity are introduced, and a review of fuzzy cluster validity indices available in the literature are presented, and extensive comparisons of the mentioned indices are conducted in conjunction with the Fuzzy C-Means clustering algorithm.

489 citations

Journal ArticleDOI
TL;DR: Experimental results show that fuzzy–rough reduction is more powerful than the conventional rough set-based approach, and classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method.

467 citations


"Enforcement of Rough Fuzzy Clusteri..." refers methods in this paper

  • ...The clustering approach [1,2,22] can be classified into two classifications such as soft and hard clustering....

    [...]

Journal ArticleDOI
01 Dec 2007
TL;DR: The RFPCM comprises a judicious integration of the principles of rough and fuzzy sets that incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy C-means and the coincident clusters of PCM.
Abstract: A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic C-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. It incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy C-means and the coincident clusters of PCM. The concept of crisp lower bound and fuzzy boundary of a class, which is introduced in the RFPCM, enables efficient selection of cluster prototypes. The algorithm is generalized in the sense that all existing variants of C-means algorithms can be derived from the proposed algorithm as a special case. Several quantitative indices are introduced based on rough sets for the evaluation of performance of the proposed C-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated both qualitatively and quantitatively on a set of real-life data sets.

220 citations