Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Data sets to which clustering is applied may be homogeneous (numerical or categorical) or heterogeneous (numerical and categorical) in nature. Handling homogeneous data is easier than heterogeneous data. We propose a novel technique for identifying initial seeds for heterogeneous data clustering, through the introduction of a unique distance measure where the distance of the numerical attributes is scaled such that it is comparable to that of categorical attributes. The proposed initial seed selection algorithm ensures selection of initial seed points from different clusters of the clustering solution which are then given as input to the modified K-means clustering algorithm along with the data set. This technique is independent of any user-defined parameter and thus can be easily applied to clusterable data sets with mixed attributes. We have also modified the K-means clustering algorithm to handle mixed attributes by incorporating our novel distance measure to handle numerical data and assigned the value one or zero when categorical data is dissimilar or similar. Finally, a comparison has been made with existing algorithms to bring out the significance of our approach. We also perform a statistical test to evaluate the statistical significance of our proposed technique. © 2019, King Fahd University of Petroleum &amp; Minerals.

Computer Science

School of Computer Science and Engineering

Chennai Campus

World University root node

Mathematics

School of Advanced Sciences

Arabian Journal for Science and Engineering

Initial Seed Selection for Mixed Data Using Modified K-means Clustering Algorithm

Recent researches in cloud discusses about the application response testing, performance testing, security testing and many more, but still there is a lack of researches addressing issues like resource utilization and user interactions in cloud SaaS testing. The load on the cloud, SaaS instance keeps varying dynamically with respect to time, it is difficult to find the exact load at a particular interval of time. One does not know where to look for the solution and where to start, this made SaaS instances non deterministic in nature. In order to find a solution for such non deterministic problems, we make use of Genetic Algorithm which is considered as a good solution for non-deterministic problems.We determine the optimized resources that a cloud instance, would need to manage the dynamic load at all times. To address the resource utilization of a group of users in Multi-Tenant Architecture (MTA), we adopt Genetic Algorithm which uses a popular technique, called neighborhood search and instance ranking policy. The basic concept of this paper is to explore the neighbors of an existing solution, that is considered as the solutions which can be obtained with a specific operation on the base population. In addition to that,this paper discusses about the ranking of all the available population and select the most highly ranked one. Instance ranking policies are aimed at minimizing the number of nodes in use or maximize the resources available to each node in an instance

International Journal of Engineering and Advanced Technology

Performance Testing in A Multi Tenant Cloud Architecture using Genetic Algorithm

Initial seed artefacts play a vital role in proper categorization of the given data set in partitioning based clustering algorithms. Hence, it is important to identify them. We propose a density with distance based method which ensures identification of seed artefacts from different clusters that leads to more accurate clustering results. Our algorithm improves on the search for initial seed artefacts iteratively until the minimum value of the sum of within sum errors, normalized by their data sizes, is ensured. This is because the initial artefacts are selected from different clusters. Here the choice of seed artefacts guarantees a global optimum clustering solution. We have compared our results with random, Wu, Cao and Khan's methods of initial seed artefact selection, to show the efficacy of our method. © 2018 The Authors

VIT Unitversity

Fulltext

Journal of King Saud University - Computer and Information Sciences

Initial seed selection for K-modes clustering – A distance and density based approach

Open issues with respect to K means algorithm are - identifying the number of clusters, initial seed concept selection, clustering tendency, handling empty clusters, identifying outliers etc. In this paper we propose a novel and simple technique considering both density and distance of the concepts in a dataset to identify initial seed concepts for clustering. Many authors have proposed different techniques to identify initial seed concepts; but our method ensures that the initial seed concepts are chosen from different clusters which are to be generated by the clustering solution. The hallmark of our algorithm is that it is a single pass algorithm which does not require any extra parameters to be estimated. Further, our seed concepts are among the actual concepts and not the mean of representative concepts as is the case in many other algorithms. We have implemented our proposed algorithm and compared the results with the interval based technique of Khan. We see that our method outperforms the interval based method. We have also compared our method with the original random K means and K Means ++ algorithms.

Journal of Computing and Information Technology

A Simple Density with Distance Based Initial Seed Selection Technique for K Means Algorithm

Urban cities are like complex systems. In order to test various policies urban models are used. A cellular automaton is used to simulate complex systems. The data used for urban planning is huge and complex because it is based on various factors like the growth in population, economy etc. So an accurate classifier is required. This paper proposes a cellular automata model, called Scalable Cellular Automata-based Classifier (SCAC), which has the capability to deal with non-conforming patterns in the binary features. It is developed on a Decision Support Elementary Cellular Automata (DS-ECA). The classifying capability of DS-ECA is accurate since it can describe very complicated decision rule in high dimension problems with less complexity. SCAC has double rule vectors and a scalable algorithm with decision function, the structure of which has two layers; the first layer is employed to evolve an input pattern into feature space using Apache Spark and the other interprets the patterns in feature space as binary answer through the decision function which is also scaled in a cluster. The algorithm proposed is a Cloud based Cluster computing model which is based on Cellular Automata. © IAEME Publication.

International Journal of Civil Engineering and Technology

Urban growth modelling using cellular automata based classifier

Vehicle classification is required to study various parameters related to traffic. It is impossible to estimate the density of vehicles, number of vehicle types etc. without vehicles classification. This paper reviews various neural network algorithms using single loop detector that can be used in real time traffic management system to classify vehicles. Since it is evident that neural network is a weak classifier, we propose a model which uses an ensembled neural network algorithm using ILD to achieve high speed and accuracy than the traditional method. © 2012 Springer-Verlag.