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

Gene co-expression analysis is an important research problem in molecular biology that helps to identify co-occurring genes in potential biological function. Clustering methods have been widely employed for this problem and hierarchical clustering based gene expression analysis has made tremendous progress in the past years. However, these methods heavily rely on proximity measures used in the clustering process. One of the major issues of hierarchical clustering is their inability to detect arbitrary shaped clusters in high dimensional spaces. Another issue is their pre-requisite of distance matrix calculation, which is not computationally efficient for large datasets. To address these issues, this paper proposes approximate similarity measures based on local neighborhood representation using minimum spanning tree. The effectiveness of proposed similarity measures is tested using hierarchical clustering algorithm. Experimental results on microarray gene expression datasets reveal that the proposed similarity measures achieve improved results in terms of clustering accuracy as well as reduced time complexity as compared to conventional distance measures. © 2021 Elsevier Ltd

Computer Science

School of Computer Science and Engineering

Chennai Campus

World University root node

Computers and Electrical Engineering

Gene expression clustering using local neighborhood-based similarity measures

Human activity recognition (HAR) from the time-series data generated by smarlt devices like smartphones and smartwathces is absolutely necessary for future intelligent health-care systems. A number of machine learning approaches have been devised for human activity recognition from such devices. However, most of the existing approaches have considered activity recognition as a supervised learning problem, which requires a training dataset for learning the activities. With ever-growing computing power of smart devices, the amount of data generated by these devises is also increasing in manifold. Thus, it becomes a tedious task to annotate the data collected from the smart devices. Being an unsupervised learning approach, cluster analysis provides meaningful insights on hidden patterns in the huge volume of data without training examples. Hence cluster analysis can be used as a preprocessing step in human activity recognition when there is no sufficient information about the number of activities. This paper presents a comparative study of three clustering algorithms such as K-means, hierarchical agglomerative clustering and Fuzzy C-means for human activity recognition problem. A method to automatically determine the number of activities is also demonstrated in this paper. Experimental results on two UCI activity recognition datasets show that FCM algorithm effectively categorizes the activities. © Springer Nature Switzerland AG 2020.

Advances in Intelligent Systems and Computing

Clustering time-series data generated by smart devices for human activity recognition

VIT Unitversity

International Journal of Computer Sciences and Engineering

Intelligent Driver Assistant System A survey

Software fault prediction is an important task in software development process which enables software practitioners to easily detect and rectify the errors in modules or classes. Various fault prediction techniques have been studied in the past and unsupervised learning methods such as clustering techniques are drawing much attention in the recent years. K-means is a well known clustering algorithm which is applied on various exploratory analysis including software fault prediction. This paper provides a comparative study on software fault prediction using K-means clustering algorithm and its variants. We use five software fault prediction datasets taken from PROMISE repository to evaluate the prediction accuracy of the clustering algorithms. Experimental results indicate that proper initial seed selection enables K-means algorithm to effectively group the faulty modules. © 2018 IEEE.

Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018

A Comparative Study of Unsupervised Learning Algorithms for Software Fault Prediction

Clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. K-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, K-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for K-means (DK-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical K-means with random initialization and improved K-means variants such as K-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that DK-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms. © 2017, Springer-Verlag London Ltd., part of Springer Nature.

Pattern Analysis and Applications

DK-means: a deterministic K-means clustering algorithm for gene expression analysis

Minimum Spanning Tree (MST) based clustering algorithms have been employed successfully to detect clusters of heterogeneous nature. Given a dataset of n random points, most of the MST-based clustering algorithms first generate a complete graph G of the dataset and then construct MST from G. The first step of the algorithm is the major bottleneck which takes O(n2) time. This paper proposes an algorithm namely MST-based clustering on partition-based nearest neighbor graph for reducing the computational overhead. By using a centroid based nearest neighbor rule, the proposed algorithm first generates a sparse Local Neighborhood Graph (LNG) and then the approximate MST is constructed from LNG. We prove that both size and computational time to construct the graph (LNG) is O(n3/2), which is a O(n) factor improvement over the traditional algorithms. The approximate MST is constructed from LNG in O(n3/2lgn) time, which is asymptotically faster than O(n2). Experimental analysis on both synthetic and real datasets demonstrates that the computational time has been reduced significantly by maintaining the quality of clusters obtained from the approximate MST. © 2017 Elsevier B.V.

Neurocomputing

Fast approximate minimum spanning tree based clustering algorithm

2017 International Conference on Computing Methodologies and Communication (ICCMC)

Optimum coordination of overcurrent relays in distribution systems using differential evolution and dual simplex methods

Management

VIT Business School

International Journal of Business Excellence

Job characteristics as determinants of career change

Clustering has become one of the important data analysis techniques for the discovery of cancer disease. Numerous clustering approaches have been proposed in the recent years. However, handling of high-dimensional cancer gene expression datasets remains an open challenge for clustering algorithms. In this paper, we present an improved graph based clustering algorithm by applying edge betweenness criterion on spanning subgraph. We carry out empirical analysis on artificial datasets and five cancer gene expression datasets. Results of the study show that the proposed algorithm can effectively discover the cancerous tissues and it performs better than two recent graph based clustering algorithms in terms of cluster quality as well as modularity index. © 2017, Springer International Publishing AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A betweenness centrality guided clustering algorithm and its applications to cancer diagnosis

Indian Journal of Science and Technology

Examining the Antecedents of Social Media user Gratification among High School and Undergraduate Students

K-means clustering algorithm is rich in literature and its success stems from simplicity and computational efficiency. The key limitation of K-means is that its convergence depends on the initial partition. Improper selection of initial centroids may lead to poor results. This paper proposes a method known as Deterministic Initialization using Constrained Recursive Bi-partitioning (DICRB) for the careful selection of initial centers. First, a set of probable centers are identified using recursive binary partitioning. Then, the initial centers for K-means algorithm are determined by applying a graph clustering on the probable centers. Experimental results demonstrate the efficacy and deterministic nature of the proposed method. © Springer India 2016.

Smart Innovation, Systems and Technologies

On careful selection of initial centers for K-means algorithm

Gene expression data clustering is an important biological process in DNA microarray analysis. Although there have been many clustering algorithms for gene expression analysis, finding a suitable and effective clustering algorithm is always a challenging problem due to the heterogeneous nature of gene profiles. Minimum Spanning Tree (MST) based clustering algorithms have been successfully employed to detect clusters of varying shapes and sizes. This paper proposes a novel clustering algorithm using Eigenanalysis on Minimum Spanning Tree based neighborhood graph (E-MST). As MST of a set of points reflects the similarity of the points with their neighborhood, the proposed algorithm employs a similarity graph obtained from k' rounds of MST (k'-MST neighborhood graph). By studying the spectral properties of the similarity matrix obtained from k'-MST graph, the proposed algorithm achieves improved clustering results. We demonstrate the efficacy of the proposed algorithm on 12 gene expression datasets. Experimental results show that the proposed algorithm performs better than the standard clustering algorithms. © 2016 Elsevier Ltd.

Computers in Biology and Medicine

Functional grouping of similar genes using eigenanalysis on minimum spanning tree based neighborhood graph

Spectral clustering is one of the most popular modern graph clustering techniques in machine learning. By using the eigenvalue analysis, spectral methods partition the given set of points into number of disjoint groups. Spectral methods are very useful in determining non-convex shaped clusters, identifying such clusters is not trivial for many traditional clustering methods including hierarchical and partitional methods. Spectral clustering may be carried out either as recursive bi-partitioning using fiedler vector (second eigenvector) or as muti-way partitioning using first k eigenvectors, where k is the number of clusters. Although spectral methods are widely discussed, there has been a little attention on which post-clustering algorithm (for eg. K-means) should be used in multi-way spectral partitioning. This motivated us to carry out an experimental study on the influence of post-clustering phase in spectral methods. We consider three clustering algorithms namely K-means, average linkage and FCM. Our study shows that the results of multi-way spectral partitioning strongly depends on the post-clustering algorithm. © Springer International Publishing Switzerland 2015.

On the impact of post-clustering phase in multi-way spectral partitioning

This paper presents a multi-secret sharing scheme using Hill cipher and random grids. The scheme is an extension of visual secret sharing scheme for gray scale images introduced in [13] by Chen. The proposed scheme provides increased security with lossless image recovery and no pixel expansion. Numerical results demonstrate the efficiency of the scheme. © 2015 IEEE.

Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015

On multi-secret sharing using Hill cipher and random grids

Minimum spanning tree (MST) based clustering algorithms have been employed successfully to detect clusters of heterogeneous nature. Given a dataset of n random points, most of the MST-based clustering algorithms first generate a complete graph G of the dataset and then construct MST from G. The first step of the algorithm is the major bottleneck which takes O(n2) time. This paper proposes two algorithms namely MST-based clustering on K-means Graph and MST-based clustering on Bi-means Graph for reducing the computational overhead. The proposed algorithms make use of a centroid based nearest neighbor rule to generate a partition-based Local Neighborhood Graph (LNG). We prove that both the size and the computational time to construct the graph (LNG) is O(n3/2), which is a O(√n) factor improvement over the traditional algorithms. The approximate MST is constructed from LNG in O(n3/2 lg n) time, which is asymptotically faster than O(n2). The advantage of the proposed algorithms is that they do not require any parameter setting which is a major issue in many of the nearest neighbor finding algorithms. Experimental results demonstrate that the computational time has been reduced significantly by maintaining the quality of the clusters obtained from the MST. © Springer International Publishing Switzerland 2015.