







The clustering of data series was already demonstrated to provide helpful information in several fields. Initial data for the period is divided into sub-clusters Recorded in the data resemblance. The grouping of data series takes 3 categories, based on which users operate in frequencies or programming interfaces on original data explicitly or implicitly with the characteristics derived from physical information or through a framework based on raw material. The bases of series data grouping are provided. The conditions for the evaluation of the outcomes of grouping are multi-purpose time constant frequently employed in dataset grouping research. A clustering method splits data into different groups so that the resemblance between organisations is better. K-means++ offers an excellent convergence rate compared to other methods. To distinguish the correlation between items the maximum distance is employed. Distance measure metrics are frequently utilized with most methods by many academics. Genetic algorithm for the resolution of cluster issues is worldwide optimization technologies in recent times. The much more prevalent partitioning strategies of large volumes of data are K-Median & K-Median methods. This analysis is focusing on the multiple distance measures, such as Euclidean, Public Square and Shebyshev, hybrid K-means++ and PSO clubs techniques. Comparison to orgorganization-basedthods reveals an excellent classification result compared to the other methods with the K++ PSO method utilizing the Chebyshev distance measure.

- Aghabozorgi, S. R., Wah, T. Y., Amini, A., & Saybani, M. R. (2011). A new approach to present prototypes in clustering of time series. In Proceedings of the International Conference on Data Science (ICDATA) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). http://eprints.um.edu.my/id/eprint/13448
- Aghabozorgi, S., & Teh, Y. W. (2014). Stock market co-movement assessment using a three-phase clustering method. Expert Systems with Applications, 41(4), 1301-1314. https://doi.org/10.1016/j.eswa.2013.08.028
- Aghabozorgi, S., Ying Wah, T., Herawan, T., Jalab, H. A., Shaygan, M. A., & Jalali, A. (2014). A hybrid algorithm for clustering of time series data based on affinity search technique. The Scientific World Journal, 2014. https://doi.org/10.1155/2014/562194
- Aghdasi, T., Vahidi, J., Motameni, H., & Inallou, M. M. (2014). K-harmonic means data clustering using combination of particle swarm optimization and tabu search. International Journal of Mechatronics, Electrical and Computer Technology, 4(11), 485-501.
- Akojwar, S. G., & Kshirsagar, P. R. (2016). Performance evolution of optimization techniques for mathematical benchmark functions. International Journal of Computers, 1.
- Chuang, L. Y., Lin, Y. D., & Yang, C. H. (2012). Data clustering using chaotic particle swarm optimization. IAENG International Journal of Computer Science, 39(2), 208-213.
- Danesh, M., Naghibzadeh, M., Totonchi, M. R. A., Danesh, M., Minaei, B., & Shirgahi, H. (2011). Data clustering based on an efficient hybrid of K-harmonic means, PSO and GA. In Transactions on computational collective intelligence IV (pp. 125-140). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21884-2_2
- Darkins, R., Cooke, E. J., Ghahramani, Z., Kirk, P. D., Wild, D. L., & Savage, R. S. (2013). Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm. PloS one, 8(4), e59795. https://doi.org/10.1371/journal.pone.0059795
- Ghassempour, S., Girosi, F., & Maeder, A. (2014). Clustering multivariate time series using hidden Markov models. International journal of environmental research and public health, 11(3), 2741-2763. https://doi.org/10.3390/ijerph110302741
- H.Kremer, P.Kranen, T.Jansen, T.Seidl, A.Bifet, G.Holmes, B. Pfahringer, An effective evaluation measure for clustering on evolving data streams, in: Proceedings of the 17thACMSIGKDD international conference on Knowledge Discovery and Data Mining, 2011,pp.868–876.
- Kshirsagar, P. R., Akojwar, S. G., & Dhanoriya, R. A. M. K. U. M. A. R. (2017). Classification of ECG-signals using artificial neural networks. In Proceedings of International Conference on Intelligent Technologies and Engineering Systems, Lecture Notes in Electrical Engineering (Vol. 345).
- Kshirsagar, P. R., Manoharan, H., Al-Turjman, F., & Kumar, K. (2020). Design and testing of automated smoke monitoring sensors in vehicles. IEEE Sensors Journal.
- Kshirsagar, P., & Akojwar, S. (2016, December). Optimization of BPNN parameters using PSO for EEG signals. In International Conference on Communication and Signal Processing 2016 (ICCASP 2016) (pp. 384-393). Atlantis Press. https://dx.doi.org/10.2991/iccasp-16.2017.59
- Kshirsagar, P., Balakrishnan, N., & Yadav, A. D. (2020). Modelling of optimised neural network for classification and prediction of benchmark datasets. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8(4), 426-435. https://doi.org/10.1080/21681163.2019.1711457
- Kshirsagar, P., Chavan, S., & Akojwar, S. (2017). Brain tumor classification and detection using neural Network. Scholars' Press.
- Kumar, M., Patel, N. R., & Woo, J. (2002, July). Clustering seasonality patterns in the presence of errors. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 557-563). https://doi.org/10.1145/775047.775129
- Lai, C. P., Chung, P. C., & Tseng, V. S. (2010). A novel two-level clustering method for time series data analysis. Expert Systems with Applications, 37(9), 6319-6326. https://doi.org/10.1016/j.eswa.2010.02.089
- Madicar, N., Sivaraks, H., Rodpongpun, S., & Ratanamahatana, C. A. (2013). Parameter-free subsequences time series clustering with various-width clusters. In 2013 5th International Conference on Knowledge and Smart Technology (KST) (pp. 150-155). IEEE. https://doi.org/10.1109/KST.2013.6512805
- Manoharan, H., Teekaraman, Y., Kshirsagar, P. R., Sundaramurthy, S., & Manoharan, A. (2020). Examining the effect of aquaculture using sensor‐based technology with machine learning algorithm. Aquaculture Research, 51(11), 4748-4758. https://doi.org/10.1111/are.14821
- Ni, L., & Jinhang, S. (2017, October). The analysis and research of clustering algorithm based on PCA. In 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (pp. 361-365). IEEE. https://doi.org/10.1109/ICEMI.2017.8265817
- Niennattrakul, V., Srisai, D., & Ratanamahatana, C. A. (2012). Shape-based template matching for time series data. Knowledge-Based Systems, 26, 1-8. https://doi.org/10.1016/j.knosys.2011.04.015
- Oh, S., Song, S., Grabowski, G., Zhao, H., & Noonan, J. P. (2013). Time series expression analyses using RNA-seq: a statistical approach. BioMed research international, 2013. https://doi.org/10.1155/2013/203681
- Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern recognition, 44(3), 678-693. https://doi.org/10.1016/j.patcog.2010.09.013
- Rai, P., & Singh, S. (2010). A survey of clustering techniques. International Journal of Computer Applications, 7(12), 1-5.
- Rakthanmanon, T., Keogh, E. J., Lonardi, S., & Evans, S. (2012). MDL-based time series clustering. Knowledge and information systems, 33(2), 371-399. https://doi.org/10.1007/s10115-012-0508-7
- Ran, L., Yong, Y., & Na, Z. C. (2013). The K-means clustering algorithm based on chaos particle swarm. Journal of Theoretical and Applied Information Technology, 48(2). https://doi.org/10.1109/JSEN.2020.3044604
- S. Akojwar and P. Kshirsagar, “A Novel Probabilistic-PSO Based Learning Algorithm for Optimization of Neural Networks for Benchmark Problems”, Wseas Transactions on Electronics, Vol. 7, pp. 79-84, 2016.
- Seref, O., Fan, Y. J., & Chaovalitwongse, W. A. (2014). Mathematical programming formulations and algorithms for discrete k-median clustering of time-series data. INFORMS Journal on Computing, 26(1), 160-172.
- Sethi, C., & Mishra, G. (2013). A Linear PCA based hybrid K-Means PSO algorithm for clustering large dataset. International Journal of Scientific & Engineering Research, 4(6), 1559-1566.
- Xia, X., Ye, X., & Zhang, J. (2012, November). Optimal metering plan of measurement and verification for energy efficiency lighting projects. In 2012 Southern African Energy Efficiency Convention (SAEEC) (pp. 1-8). IEEE. https://doi.org/10.1109/SAEEC.2012.6408588
- Xu, K., Jiang, Y., Tang, M., Yuan, C., & Tang, C. (2013). PRESEE: an MDL/MML algorithm to time-series stream segmenting. The Scientific World Journal, 2013. https://doi.org/10.1155/2013/386180
- Zakaria, J., Mueen, A., & Keogh, E. (2012, December). Clustering time series using unsupervised-shapelets. In 2012 IEEE 12th International Conference on Data Mining (pp. 785-794). IEEE. https://doi.org/10.1109/ICDM.2012.26
- Zakaria, J., Rotschafer, S., Mueen, A., Razak, K., & Keogh, E. (2012, April). Mining massive archives of mice sounds with symbolized representations. In Proceedings of the 2012 SIAM International Conference on Data Mining (pp. 588-599). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972825.51
- Zhang, X., Liu, J., Du, Y., & Lv, T. (2011). A novel clustering method on time series data. Expert Systems with Applications, 38(9), 11891-11900. https://doi.org/10.1016/j.eswa.2011.03.081
