Volume 3, Issue 2, April 2015, Pages 79 - 88
Optimal Number of Clusters for Fast Similarity Search Considering Transformations of Time Varying Data
Toshiichiro Iwashita, Teruhisa Hochin, Hiroki Nomiya
Available Online 1 April 2015.
- https://doi.org/10.2991/ijndc.2015.3.2.2How to use a DOI?
- Time series, Transformation, Retrieval, Cluster, Optimal number
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/ijndc.2015.3.2.2 ID - Iwashita2015 ER -