Time series k -means: A new k -means type smooth subspace clustering for time series data

Huang, Xiaohui; Ye, Yunming; Xiong, Liansong; Lau, Raymond Y. K.; Jiang, Nan; Wang, Shaokai

doi:10.1016/j.ins.2016.05.040

Cited by 89 publications

(71 citation statements)

References 21 publications

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“…Also, we improve the method by incorporating weights in the calculation of similarity, just as TKmeans does to K-means. First of all, we briefly introduce TSKmeans [7]. TSKmeans is a Kmeans incorporated with weights.…”

Section: Weight Self-constructing Clusteringmentioning

confidence: 99%

A Time Series Forecasting Method

et al. 2017

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This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

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Section: Weight Self-constructing Clusteringmentioning

confidence: 99%

A Time Series Forecasting Method

et al. 2017

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“…[18] Finally, the last group utilizes clustering technique in order to employ centres of clusters as the typical patterns. A paper [19] is concerned with k-means in order to adjust it to seek for similar sections in chaotic time series; the modified algorithm is dubbed with TSkmeans (Time Series k-means). The paper [20] also considers k-means to predict chaotic time series; it summarizes results by various investigators for forecasting Australia's national electricity market prices; an extended version of the results may be found in Gromov and Borisenko.…”

Section: Related Workmentioning

confidence: 99%

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Gromov

Voronin

Gatylo

et al. 2017

AIR

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The paper deals with a novel algorithm used to improve identification quality of clusters generated by predictive clustering algorithm as a tool to identify states preceding to bifurcations for a system governed by von Karman equations. To construct bifurcation precursors, solutions (of the equations) observed on bifurcation paths are clustered; centers of the clusters constitute a set of bifurcation precursors. To decrease identification error rate, quality of each precursor is assessed with the employment of an additional, validation set. The paper concerns with two approaches to this procedure; the first one employs a single number to assess identification value of a cluster in order to delete those with low identification values. The second approach uses proposed knowledge extraction procedure to ascertain rules of replacement of the precursors chosen by the algorithm (active) by more efficient one. A wide-ranging simulation reveals that the best variant (provided that the Wishart clustering algorithm is utilized) is the replacement of the active cluster in conjunction local normalization of data. The optimal parameters values for both algorithms, arriving at essentially decreased identification errors.

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“…Many clustering methods have been proposed for time series data [8] [9][10] [7]. Among them, TSKmeans [7] is the most recently published.…”

Section: Related Workmentioning

confidence: 99%

“…Among them, TSKmeans [7] is the most recently published. TSKmeans is a K-means incorporated with weights.…”

Section: Related Workmentioning

confidence: 99%

“…In the process of clustering time series data, using the same weight for each dimension may cause bad effects. To deal with this difficulty, Huang et al proposed TSKmeans [7], which is K-means with weights, to assign different weights to different dimensions of the data. A similarity measure based on the weighted Euclidean distance was adopted.…”

Section: Introductionmentioning

confidence: 99%

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A Weight-Based Clustering Method

2017

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This paper proposes a weight-based self-constructing clustering method for time series data. Selfconstructing clustering processes all the data points incrementally. If a data point is not similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it forms a new cluster of its own; (2) otherwise, the point is removed from the cluster it currently belongs to before a new cluster is formed. However, if a data point is similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it is added to the most similar cluster; (2) otherwise, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned and considered in the calculations of similarity between data points and clusters. Experimental results show that our proposed approach performs more effectively than other methods for real world time series datasets.

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Time series k -means: A new k -means type smooth subspace clustering for time series data

Cited by 89 publications

References 21 publications

A Time Series Forecasting Method

A Time Series Forecasting Method

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

A Weight-Based Clustering Method

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