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Volume 1, Issue 4 (July 2022)

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Hybrid Clustering Approach for Time Series Data
R Kumaar Prathipati
 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad - India
V Harsha Shastri
 Department of Computer Systems and Engineering, Loyola Academy, Secunderabad, Telangana - India
 rk30111972@klh.edu.in 
 Corresponding Author
Madhavi Kolukuluri
 Department of Computer Science and Engineering, NSRIT, Visakhapatnam - India
Radha Dharavathu
 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad – India
Donthireddy Sudheer Reddy
 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad – India
B N Siva Rama Krishna
 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad – India
ISSN(e): 2790-296X
ISSN(p): 2957-5826
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Prof. Dr. Emad Al-Mulla
 
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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.

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Keywords:  Clustering, Data mining, Distance measure, K-means, K-median, Time series data

How to Cite:
Prathipati, R. K., Shastri, V. H., Kolukuluri, M., Dharavathu, R., Reddy, D. S., & Krishna, B. N. S. R. (2022). Hybrid Clustering Approach for Time Series Data. Biomedicine and Chemical Sciences, 1(4), 207–214. https://doi.org/10.48112/bcs.v1i4.84 
 
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