Conference Papers

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    Parallelized K-Means clustering algorithm for self aware mobile Ad-hoc networks
    (2011) Thomas, L.; Manjappa, K.; Annappa, B.; Guddeti, G.R.M.
    Providing Quality of Service (QoS) in Mobile Ad-hoc Network (MANET) in terms of bandwidth, delay, jitter, throughput etc., is critical and challenging issue because of node mobility and the shared medium. The work in this paper predicts the best effective cluster while taking QoS parameters into account. The proposed work uses K-Means clustering algorithm for automatically discovering clusters from large data repositories. Further, iterative K-Means clustering algorithm is parallelized using Map-Reduce technique in order to improve the computational efficiency and thereby predicting the best effective cluster. Hence, parallel K-Means algorithm is explored for finding the best effective cluster containing the hops which lies in the best cluster with the best throughput in self aware MANET. Copyright © 2011 ACM.
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    Application of parallel K-means clustering algorithm for prediction of optimal path in self aware mobile ad-hoc networks with link stability
    (2011) Thomas, L.; Annappa, B.
    Providing Quality of Service (QoS) in terms of bandwidth, delay, jitter, throughput etc., for Mobile Ad-hoc Network (MANET) which is the autonomous collection of nodes, is challenging issue because of node mobility and the shared medium. This work is to predict the Optimal link based on the link stability which is the number of contacts between 2 pair of nodes that can be effectively applied for prediction of optimal effective path while taking QoS parameters into account to reach the destination using the application of K-Means clustering algorithm for automatically discovering clusters from large data repositories which is parallelized using Map-Reduce technique in order to improve the computational efficiency and thereby predicting the optimal effective path from source to sink. The work optimizes the previous result by pre-assigning task for finding the best stable link in MANET and then work is explored only on that stable link hence, by doing so we are able to predict the optimal path in more time efficient way. © 2011 Springer-Verlag.
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    Recommender system based on Hierarchical Clustering algorithm Chameleon
    (Institute of Electrical and Electronics Engineers Inc., 2015) Gupta, U.; Patil, N.
    Recommender Systems are becoming inherent part of today's e-commerce applications. Since recommender system has a direct impact on the sales of many products therefore Recommender system plays an important role in e-commerce. Collaborative filtering is the oldest techniques used in the recommender system. A lot of work has been done towards the improvement of collaborative filtering which comprises of two components User Based and Item Based. The basic necessity of today's recommender system is accuracy and speed. In this work an efficient technique for recommender system based on Hierarchical Clustering is proposed. The user or item specific information is grouped into a set of clusters using Chameleon Hierarchical clustering algorithm. Further voting system is used to predict the rating of a particular item. In order to evaluate the performance of Chameleon based recommender system, it is compared with existing technique based on K-means clustering algorithm. The results demonstrates that Chameleon based Recommender system produces less error as compared to K-means based Recommender System. © 2015 IEEE.
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    Unsupervised learning method for mineral identification from hyperspectral data
    (Springer, 2021) Prabhavathy, P.; Tripathy, B.K.; Venkatesan, M.
    Hyperspectral imagery is one of the research area in the field of Remote sensing. Hyperspectral sensors record reflectance (also called spectra signature) of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of Remote sensing of Hyperspectral data. EO-1 hyperion dataset is unlabeled data. Various types of clustering algorithms are proposed to identify minerals. In this work principal component analysis is used to reduced it’s dimension by reducing bands. Hard-clustering and soft-clustering algorithms are applied on given data to classify the minerals into classes. K-means is hard type of clustering which classify only non-overlapping cluster however, PFCM is soft type of clustering which allow a data points to belongs more than one cluster. Further, results are compared using cluster validity index using DBI value. Both clustering algorithms are experiments on original HSI image and reduced bands. Result shows that PFCM is perform better than K-means for the both type of images. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.