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Inventi Impact - Algorithm

Articles

  • Inventi:eal/87/14
    A HYBRID ALGORITHM FOR CLUSTERING OF TIME SERIES DATA BASED ON AFFINITY SEARCH TECHNIQUE
    Saeed Aghabozorgi, Teh Ying Wah, Tutut Herawan, Hamid A Jalab, Mohammad Amin Shaygan, Alireza Jalali

    Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters basedon similarity intime.The subclusters are thenmergedusing the k-Medoids algorithmbased on similarity in shape. Thismodel has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, themodel is tested extensively using syntactic and real-world time series datasets.

    How to Cite this Article
    CC Compliant Citation: Saeed Aghabozorgi, Teh Ying Wah, Tutut Herawan, Hamid A. Jalab, Mohammad Amin Shaygan, and Alireza Jalali, “A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique,” The Scientific World Journal, vol. 2014, Article ID 562194, 12 pages, 2014. doi:10.1155/2014/562194. Copyright © 2014 Saeed Aghabozorgi et al. This is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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