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Author

Parvathavarthini Balasubramanian

Bio: Parvathavarthini Balasubramanian is an academic researcher. The author has contributed to research in topics: Rough set & Fuzzy classification. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Journal Article
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.

3 citations


Cited by
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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

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

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