Optimal Number of Clusters for Fast Similarity Search Considering Transformations of Time Varying Data

DOI

10.2991/ijndc.2015.3.2.2How to use a DOI?

Keywords

Time series, Transformation, Retrieval, Cluster, Optimal number

Abstract

This paper proposes a method of determining the optimal number of clusters dividing the multiple transformations for the purpose of the efficient processing of query against the results of applying the transformations to time series. In this paper, the moving average is used as a transformation for simplicity. The model of query time to the number of clusters is constructed for determining the optimal number of clusters. As the query time could be represented with the concave function of the number of clusters, it is shown that the optimal number of clusters for the best query time can be obtained. The verification experiment confirms the validity of the model constructed. It is revealed that the optimal number of clusters could be determined by the times obtained from a single query execution.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Toshiichiro Iwashita
AU  - Teruhisa Hochin
AU  - Hiroki Nomiya
PY  - 2015
DA  - 2015/04/01
TI  - Optimal Number of Clusters for Fast Similarity Search Considering Transformations of Time Varying Data
JO  - International Journal of Networked and Distributed Computing
SP  - 79
EP  - 88
VL  - 3
IS  - 2
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2015.3.2.2
DO  - 10.2991/ijndc.2015.3.2.2
ID  - Iwashita2015
ER  -