Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal
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Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset G. Surya Narayana 1
& Kamakshaiah Kolli
2
Received: 19 November 2019 / Revised: 12 August 2020 / Accepted: 25 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Many conventional optimization approaches concentrate more on addressing only one appropriate solution. Thus, these methods were to be utilized often, hence there were no chances of producing the intended solution. Therefore, the issue of multimodal optimization has to be considered. So, to reduce the difficulties by the clustering and further, it followed by the optimization technique. Here, the variety of real-time and artificial techniques are used. Using the FCDP-Fast Clustering with Density Peak, we calculate the density values after determining the center with the help of objective function. Then, the fuzzy clustering is applied to form the clustered groups with the density and center values. Finally, we optimize the data using the CDE-Crowding Differential Evaluation methodology. Performance analysis is then proceeded with some existing methods by using the performance metrics like NMI and ARI. After validation, it concluded that the proposed method was superior to the existing method. Keywords K-means clustering . Multimodal optimization . Crowding differential evaluation . Density value . Center distance
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* G. Surya Narayana [email protected]
1
Cse Department, Sreyas Institute of Engineering and Technology, Hyderabad, India
2
Department of CSE, Geethanjali College of Engineering and Technology, Cheeryal Village, Hyderabad, India
Multimedia Tools and Applications
mining and anonymization have become sough one of the topics. The proposed method too is willing to take up the clustering and optimization of the big datasets by calculating the centers based on Fast Density Peak Clustering FCDP and then, cluster it by using the fuzzy logic clustering. As far as the optimization is concerned, we will make use of the CDE Crowding Differential Evaluation methodology based on the Evolutionary multimodal optimization methodology. Clustering of data in a definite manner was a tough and puzzling job due to the distinctive type of clear-cut features: no natural order. That case wished to offer a two-step process called PM-FGCA-Partition-cum-Merge dependent Fuzzy Genetic Clustering Algorithm for clear-cut statistics [16]. To estimate the clustering fulfilment, the offered PM-FGCA Partition-cum-Merge dependent Fuzzy Genetic Clustering Algorithm was associated with few remaining approaches like fuzzy k-modes procedure, k-modes procedure, non-dominated sorting genetic procedure, and genetic fuzzy k-modes procedure using fuzzy membership chromosomes. NMI - N
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