Abstract Big data is a collection of large volume of data and extract similar data points from large dataset. Clustering is an essential data mining technique for examining large volume of data. Several techniques have been developed for handling big dataset. However, with much time consumption and space complexity, accuracy is said to be compromised. In order to improve clustering accuracy with less complexity, Sørensen-Dice Indexing based Weighted Iterative X-means Clustering (SDI- WIXC) technique is introduced. SDI-WIXC technique is used for grouping the similar data points with higher clustering accuracy and minimal time. First, number of data points is collected from big dataset. Then, along with the weight value, the given dataset is partitioned into ‘X’ number of clusters. Next, based on the similarity measure, Weighted Iterated X-means Clustering (WIXC) is applied for clustering data points. Sørensen-Dice Indexing Process is used for measuring similarity between cluster weight value and data points. Upon similarity found between weight value of cluster and data point, data points are grouped into a specific cluster. Besides, the WIXC method also improves the cluster assignments through repeated subdivision using Bayesian probability criterion. This in turn helps to group all data points and hence, improving the clustering accuracy. Experimental evaluation is carried out with number of factors such as clustering accuracy, clustering time and space complexity with respect to the number of data points. The experimental results reported that the proposed SDI- WIXC technique obtains high clustering accuracy with minimum time as well as space complexity.

References

[1] Bharill N., Tiwari A., and Malviya A., “Fuzzy- Based Scalable Clustering Algorithms for Handling Big Data Using Apache Spark,” IEEE Transactions on Big Data, vol. 2, no. 4, pp .339- 352, 2016.

[2] Bu F., Chen Z., Li p., Tang T., and Zhang Y., “A High-Order CFS Algorithm for Clustering Big Data,” Mobile Information Systems, vol. 2016, pp.1-8, 2016.

[3] Cui X., Zhu P., Yang X., Li K., and Ji C., “Optimized Big Data K-Means Clustering using Mapreduce,” The Journal of Supercomputing, vol. 7, no. 3, pp. 1249-1259, 2014.

[4] Esteves R., Hacker T., and Rong C., “A New Approach for Accurate Distributed Cluster Analysis for Big Data: Competitive K-Means,” International Journal of Big Data Intelligence, vol. 1, no. 1-2, pp. 50-64, 2014.

[5] Genuer R., Poggi J., Tuleau-Malot C., and Villa- Vialaneix N., “Random Forests for Big Data,” Big Data Research, vol. 9, pp. 28-46, 2017.

[6] Hu R., Dou W., and Liu J., “ClubCF: A Clustering-Based Collaborative Filtering Approach for Big Data Application,” IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 3, pp. 302-313, 2014.

[7] Jain M. and Verma C., “Adapting K-Means for Clustering in Big Data,” International Journal of Computer Applications, vol. 101, no. 1, pp. 19- 24, 2014.

[8] Kuru K. and Khan W., “Novel Hybrid Object- Based Non-Parametric Clustering Approach for Grouping Similar Objects in Specific Visual Domains,” Applied Soft Computing, vol. 62, pp. 667-701, 2018.

[9] Li Z., Hu F., Schnase J., Duffy D., Lee T., Bowen M., and Yang S., “A Spatiotemporal Indexing Approach for Efficient Processing of Big Array- Sørensen-Dice Similarity Indexing based Weighted Iterative Clustering for ... 21 Based Climate Data With Mapreduce,” International Journal of Geographical Information Science, vol. 31, no. 1, pp. 1-19, 2016.

[10] Liu H., Wu J., Liu T., Tao D., Fu Y., “Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 5, pp. 1129-1143, 2017.

[11] Liu W., Ye M., Wei J., and Hu X., “Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1-14, 2017.

[12] Maghawry A., Omar Y., and Badr A., “Self- Organizing Map vs Initial Centroid Selection Optimization to Enhance K-Means with Genetic Algorithm to Cluster Transcribed Broadcast News Documents,” The International Arab Journal of Information Technology, vol. 17, no. 3, pp. 316-324, 2020.

[13] McParland D. and Gormley I., “Model Based Clustering for Mixed Data: Clustmdm,” Advances in Data Analysis and Classification, vol. 10, no. 2, pp. 155-169, 2016.

[14] Rehioui H., Idrissi A., Abourezq M., and Zegrari F., “DENCLUE-IM: A New Approach for Big Data Clustering,” Procedia Computer Science, vol. 83, pp. 560-567, 2016.

[15] Rikhtehgaran R. and Kazemi I., “The Determination of Uncertainty Levels In Robust Clustering of Subjects with Longitudinal Observations Using the Dirichlet Process Mixture,” Advances in Data Analysis and Classification, vol. 10, no. 4, pp. 541-562, 2016.

[16] Scrucca L. and Raftery A., “Improved Initialisation of Model-Based Clustering Using Gaussian Hierarchical Partitions,” Advances in Data Analysis and Classification, vol. 9, no. 4, pp. 447-460, 2015.

[17] Shao W., Salim F., Song A., and Bouguettaya A., “Clustering Big Spatiotemporal-Interval Data,” IEEE Transactions on Big Data, vol. 2, no. 3, pp. 90-203, 2016.

[18] Sreedhar C., Kasiviswanath N., Reddy P., “Clustering Large Datasets Using K-Means Modified Inter and Intra Clustering (KM-I2C) in Hadoop,” Journal of Big Data, vol. 4, no. 27, pp. 1-19, 2017.

[19] Tortora C., Summa M., Marino M., Palumbo F., “Factor Probabilistic Distance Clustering (FPDC): A New Clustering Method,” Advances in Data Analysis and Classification, vol. 10, no. 4, pp. 441-464, 2016.

[20] Traganitis P., Slavakis K., and Giannakis G., “Sketch and Validate for Big Data Clustering,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 4, pp. 678 -690, 2015.

[21] Tsapanos N., Tefas A., Nikolaidis N., Iosifidis A, and Pitas I., “Fast Kernel Matrix Computation for Big Data Clustering,” Procedia Computer Science, vol. 51, pp. 2445-2452, 2015.

[22] Wikipedia, Bayesian Information Criterion, https://en.wikipedia.org/wiki/Bayesian_informati on_criterion#:~:text=In%20statistics%2C%20the %20Bayesian%20information,the%20lowest%20 BIC%20is%20preferred, Last Visited, 2021.

[23] Wu J., Wu Z., Cao J., Liu H., Chen G., and Zhang Y., “Fuzzy Consensus Clustering With Applications on Big Data,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1430-1445. 2017.

[24] Ye M., Liu W., Wei J., and Hu X., “Fuzzy ������- Means and Cluster Ensemble with Random Projection for Big Data Clustering,” Mathematical Problems in Engineering, vol. 2016, pp. 1-13, 2016.

[25] Yu Y., Zhao J., Wang X., Wang Q., and Zhang Y., “An Efficient Distributed Density-Based Clustering for Big Data Using Hadoop,” International Journal of Distributed Sensor Networks, vol. 2015, pp. 1-13, 2015.

[26] Zhang Q. and Chen Z., “A Weighted Kernel Possibilistic C-Means Algorithm Based on Cloud Computing for Clustering Big Data,” International Journal of Communication Systems, vol. 27, no. 9, pp. 1378-1391, 2014. 22 The International Arab Journal of Information Technology, Vol. 19, No. 1, January 2022 KalyanaSaravanan Annathurai is currently working as an Assistant Professor in the School of computer technology and applications at Kongu Engineering College PerunduraiTamilnadu India. He obtained Msc (Applied Science- Computer Technology) from Barathiyar University and M.E. (Software Engineering) at Anna University (University Campus). Currently pursuing his research in Anna University. He has more than 13years of teaching experience. Also he is responsible for Industry institute participation cell in the department consultancy activities. Established Internet of Things Lab and High Performance Computing Lab (Parallel Programming) in the department for the regular practice and research Also centre for excellence in IoT in collaboration with C-DAC Bangalore at KEC .He has organized several workshops on, Big Data Analytics, Web Technology, Cloud security, R-Toolsin his Institution. Presented several papers at National and International conferences in india and abroad. He has guided a number of research-oriented as well as application oriented projects. Tamilarasi Angamuthu currently working as a Professor and Head in Department of Computer Applications in Kongu Engineering College. She has more than 25 years of experience in teaching and research in mathematics and computer science. She guided more than twenty scholars and publications in various reputed journals. Also she was a doctoral committee member/Thesis evaluator for the various universities in India and abroad.