Hybrid Clustering Approach for Time Series Data

Authors

  • 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 https://orcid.org/0000-0002-7459-5401
  • 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 https://orcid.org/0000-0002-6039-211X
  • Donthireddy Sudheer Reddy Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad – India https://orcid.org/0000-0001-6376-2705
  • B N Siva Rama Krishna Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad – India

DOI:

https://doi.org/10.48112/bcs.v1i4.84

Abstract

Abstract Views: 150

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.

Keywords:

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

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References

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Hybrid Clustering Approach for Time Series Data

Published

2022-10-01

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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Articles