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A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data

  • Adriana Navajas-GuerreroEmail author
  • Diana Manjarres
  • Eva Portillo
  • Itziar Landa-Torres
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

Clustering methods have become popular in the last years due to the need of analyzing the high amount of collected data from different fields of knowledge. Nevertheless, the main drawback of clustering is the selection of the optimal method along with its internal parameters in an unsupervised environment. In the present paper, a novel heuristic approach based on the Harmony Search algorithm aided with a local search procedure is presented for simultaneously optimizing the best clustering algorithm (K-means, DBSCAN and Hierarchical clustering) and its optimal internal parameters based on the Silhouette index. Extensive simulation results show that the presented approach outperforms the standard clustering configurations and also other works in the literature in different Time Series and synthetic databases.

Keywords

Harmony Search Clustering Internal parameters configuration Optimization Time series clustering 

Notes

Acknowledgments

This research has been supported by a TECNALIA Research and Innovation PhD Scholarship, ELKARTEK program (SENDANEU KK-2018/00032) and the HAZITEK program (DATALYSE ZL-2018/00765) of the Basque Government.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Adriana Navajas-Guerrero
    • 1
    Email author
  • Diana Manjarres
    • 1
  • Eva Portillo
    • 2
  • Itziar Landa-Torres
    • 3
  1. 1.Tecnalia Research and InnovationDerioSpain
  2. 2.Department of Automatic Control and System Engineering, School of EngineeringUniversity of the Basque Country, UPV/EHUBilbaoSpain
  3. 3.Petronor Innovación S.L.MuskizSpain

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