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B. Parvathavarthini

Bio: B. Parvathavarthini is an academic researcher from St. Joseph's College of Engineering. The author has contributed to research in topics: Rough set & k-medians clustering. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal ArticleDOI
TL;DR: Decision theory is propagated, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks.
Abstract: Cluster validation is an essential technique in all cluster applications. Several validation methods measure the accuracy of cluster structure. Typical methods are geometric, where only distance and membership form the core of validation. Yao's decision theory is a novel approach for cluster validation, which evolved loss calculations and probabilistic based measure for determining the cluster quality. Conventional rough set algorithms have utilized this validity measure. This paper propagates decision theory, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques. Experiments with synthetic and UCI datasets have been performed, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks. The proposed index can also be applied to other clustering algorithms and extends the usefulness in business oriented data mining.

2 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

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