LNCS 2810 Fuzzy Clustering of Short Time Series and Unevenly Distributed Sampling Points 1st Edition by Carla Möller Levet, Frank Klawonn, Kwang Hyun Cho, Olaf Wolkenhau ISBN 3540452311 9783540452317

LNCS 2810 – Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points 1st Edition by Carla S. Möller-Levet, Frank Klawonn, Kwang-Hyun Cho, Olaf Wolkenhau – Ebook PDF Instant Download/Delivery. 3540452311, 9783540452317

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Product details:

ISBN 10: 3540452311

ISBN 13: 9783540452317

Author:  Carla S. Möller-Levet, Frank Klawonn, Kwang-Hyun Cho, Olaf Wolkenhau 

LNCS 2810 – Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points 1st Edition: This paper proposes a new algorithm in the fuzzy-c-means family, which is designed to cluster time-series and is particularly suited for short time-series and those with unevenly spaced sampling points. Short time-series, which do not allow a conventional statistical model, and unevenly sampled time-series appear in many practical situations. The algorithm developed here is motivated by common experiments in molecular biology. Conventional clustering algorithms based on the Euclidean distance or the Pearson correlation coefficient are not able to include the temporal information in the distance metric. The temporal order of the data and the varying length of sampling intervals are important and should be considered in clustering time-series. The proposed short time-series (STS) distance is able to measure similarity of shapes which are formed by the relative change of amplitude and the corresponding temporal information. We develop a fuzzy time-series (FSTS) clustering algorithm by incorporating the STS distance into the standard fuzzy clustering scheme. An example is provided to demonstrate the performance of the proposed algorithm.

 

LNCS 2810 – Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points 1st Edition Table of contents:

1 Introduction

2 Short Time-Series Distance

3 Fuzzy Short Time-Series Clustering Algorithm

4 Illustrative Example

5 Conclusions

 

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