Abrar Alvi

Work place: Department of CSE, Badnera, Amravati, India

E-mail: abrar_alvi@rediffmail.com


Research Interests: Natural Language Processing, Programming Language Theory


Dr. A. S. Alvi graduated from Sant Gadge Baba Amravati University, Amravati in Computer Science and Engineering. He got His Master and Ph.D. degree form the same university. Currently he is working as Professor in Computer Science and Engineering at PRMIT &R, Badnera, and Amravati. He has more 20 years of teaching experience. He has published more than 25 papers in international journals and conferences. His area of interest is Artificial intelligence and Algorithms. His interest also lies in Natural Language Processing. He is a Life time member of ISTE and IET professional bodies. He is also a research guide at SGB, Amravati University, Amravati.

Author Articles
Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

By Anand Khandare Abrar Alvi

DOI: https://doi.org/10.5815/ijisa.2018.07.05, Pub. Date: 8 Jul. 2018

Analyzing data is a challenging task nowadays because the size of data affects results of the analysis. This is because every application can generate data of massive amount. Clustering techniques are key techniques to analyze the massive amount of data. It is a simple way to group similar type data in clusters. The key examples of clustering algorithms are k-means, k-medoids, c-means, hierarchical and DBSCAN. The k-means and DBSCAN are the scalable algorithms but again it needs to be improved because massive data hampers the performance with respect to cluster quality and efficiency of these algorithms. For these algorithms, user intervention is needed to provide appropriate parameters as an input. For these reasons, this paper presents modified and efficient clustering algorithm. This enhances cluster’s quality and makes clusters more cohesive using domain knowledge, spectral analysis, and split-merge-refine techniques. Also, this algorithm takes care to minimizing empty clusters. So far no algorithm has integrated these all requirements that proposed algorithm does just as a single algorithm. It also automatically predicts the value of k and initial centroids to have minimum user intervention with the algorithm. The performance of this algorithm is compared with standard clustering algorithms on various small to large data sets. The comparison is with respect to a number of records and dimensions of data sets using clustering accuracy, running time, and various clusters validly measures. From the obtained results, it is proved that performance of proposed algorithm is increased with respect to efficiency and quality than the existing algorithms.

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