TY - GEN
T1 - SIFCM-Shape
T2 - Intelligent Systems Conference, IntelliSys 2021
AU - Avni, Chen
AU - Herman, Maya
AU - Levi, Ofer
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Time-Series clustering is an important and challenging problem in data mining that is used to gain an insight into the mechanism that generate the time series. Large volumes of time series sequences appear in almost every fields including astronomy, biology, meteorology, medicine, finance, robotics, engineering and others. With the increase of time series data availability and volume, many time series clustering algorithms have been proposed to extract valuable information. The Time Series Clustering algorithms can organized into three main groups depending upon whether they work directly on raw data, with features extracted from data or with model built to best reflect the data. In this article, we present a novel algorithm, SIFCM-Shape, for clustering correlated time series. The algorithm presented in this paper is based on K-Shape and Fuzzy c-Shape time series clustering algorithms. SIFCM-Shape algorithm improves K-Shape and Fuzzy c-Shape by adding a fuzzy membership degree that incorporate into clustering process. Moreover it also takes into account the correlation between time series. Hence the potential is that the clustering results using this method are expected to be more accurate for related time-series. We evaluated the algorithm on UCR real time series datasets and compare it between K-Shape and Fuzzy C-shape. Numerical experiments on 48 real time series data sets show that the new algorithm outperforms state-of-the-art shape-based clustering algorithms in terms of accuracy.
AB - Time-Series clustering is an important and challenging problem in data mining that is used to gain an insight into the mechanism that generate the time series. Large volumes of time series sequences appear in almost every fields including astronomy, biology, meteorology, medicine, finance, robotics, engineering and others. With the increase of time series data availability and volume, many time series clustering algorithms have been proposed to extract valuable information. The Time Series Clustering algorithms can organized into three main groups depending upon whether they work directly on raw data, with features extracted from data or with model built to best reflect the data. In this article, we present a novel algorithm, SIFCM-Shape, for clustering correlated time series. The algorithm presented in this paper is based on K-Shape and Fuzzy c-Shape time series clustering algorithms. SIFCM-Shape algorithm improves K-Shape and Fuzzy c-Shape by adding a fuzzy membership degree that incorporate into clustering process. Moreover it also takes into account the correlation between time series. Hence the potential is that the clustering results using this method are expected to be more accurate for related time-series. We evaluated the algorithm on UCR real time series datasets and compare it between K-Shape and Fuzzy C-shape. Numerical experiments on 48 real time series data sets show that the new algorithm outperforms state-of-the-art shape-based clustering algorithms in terms of accuracy.
KW - Big data
KW - Heart disease detection
KW - K-Shape
KW - Time series clustering
UR - http://www.scopus.com/inward/record.url?scp=85113444927&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-82196-8_30
DO - https://doi.org/10.1007/978-3-030-82196-8_30
M3 - Conference contribution
SN - 9783030821951
T3 - Lecture Notes in Networks and Systems
SP - 404
EP - 418
BT - Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference IntelliSys
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 September 2021 through 3 September 2021
ER -