Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Hao, Chao

doi:10.1371/journal.pone.0197499

Cited by 17 publications

(5 citation statements)

References 29 publications

(31 reference statements)

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“…We use the DTW distance when clustering the time-series data and the Euclidean distance when clustering non-time-series data. Discussion for Euclidean distances can be found in [18,24], while for DTW can be found in [25,26]. The next sub-sections discuss the K-Medoids and FCM Clustering algorithms.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province)

Cindy

Cynthia

Vito

et al. 2022

J. Math. Fund. Sci.

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In Indonesia, Dengue incidence tends to increase every year but has been fluctuating in recent years. The potential for Dengue outbreaks in DKI Jakarta, the capital city, deserves serious attention. Weather factors are suspected of being associated with the incidence of Dengue in Indonesia. This research used weather and Dengue incidence data for five regions of DKI Jakarta, Indonesia, from December 30, 2008, to January 2, 2017. The study used a clustering approach on time-series and non-time-series data using K-Medoids and Fuzzy C-Means Clustering. The clustering results for the non-time-series data showed a positive correlation between the number of Dengue incidents and both average relative humidity and amount of rainfall. However, Dengue incidence and average temperature were negatively correlated. Moreover, the clustering implementation on the time-series data showed that rainfall patterns most closely resembled those of Dengue incidence. Therefore, rainfall can be used to estimate Dengue incidence. Both results suggest that the government could utilize weather data to predict possible spikes in DHF incidence, especially when entering the rainy season and alert the public to greater probability of a Dengue outbreak.

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Section: Clustering Algorithmsmentioning

confidence: 99%

Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province)

Cindy

Cynthia

Vito

et al. 2022

J. Math. Fund. Sci.

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“…In this study the DTW method was used to compare two pulse trains captured successively by a single observer, from which intra-observer reproducibility was quantified. The DTW distance is defined as (Izakian et al, 2015): where d is a distance matrix in which each element d(x i , y j ) represents the distance between two sample points (x i , y j) from the signal…”

Section: Morphological Analysis Of the Entire Waveformmentioning

confidence: 99%

Quantitative Comparison of the Performance of Piezoresistive, Piezoelectric, Acceleration, and Optical Pulse Wave Sensors

Wang

Sun

et al. 2020

Front. Physiol.

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“…Because Dynamic Time Warping (DTW) distance allows time series to be scaled locally to minimize the distance between two sequences, it can better match the characteristics of time series, which makes it widely adopted [19]. Therefore, this paper adopts DTW distance to measure data availability.…”

Section: Dis Ce X Y X Y S T T Tmentioning

confidence: 99%

Differential Privacy Trajectory Data Protection Algorithm Based on Polar Coordinate Transformation

Zhang¹,

Cai²,

Sun³

et al. 2020

Fuzzy Systems and Data Mining VI

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Differential privacy technology has been widely used in the issue of trajectory data release. Improving the availability of data release under the premise of ensuring privacy and security is one of its basic research goals. At present, most trajectory data release methods use a rectangular coordinate system to represent location information. Research has shown that the availability of published data cannot be optimized through the rectangular coordinate system. In order to improve the effect of trajectory data release, this paper proposes a differential privacy trajectory data protection algorithm based on polar coordinates. First, the stay point detection method is used to find frequent stay points in the trajectory and the key location points related to personal privacy are detected by the type of location points. Then, this paper converts the rectangular coordinate system representation of the key position points to the polar coordinate system representation, and implement differential privacy trajectory data release by adding noise to the key position points represented by the polar coordinates. Experiments show that the algorithm proposed in this paper effectively improves the usability of trajectory data on real data sets.

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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Cited by 17 publications

References 29 publications

Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province)

Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province)

Quantitative Comparison of the Performance of Piezoresistive, Piezoelectric, Acceleration, and Optical Pulse Wave Sensors

Differential Privacy Trajectory Data Protection Algorithm Based on Polar Coordinate Transformation

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