2020
DOI: 10.37899/journallamultiapp.v1i3.191
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Time Series Clustering Based on the K-Means Algorithm

Abstract: Time series is one of the forms of data presentation that is used in many studies. It is convenient, easy and informative. Clustering is one of the tasks of data processing. Thus, the most relevant currently are methods for clustering time series. Clustering time series data aims to create clusters with high similarity within a cluster and low similarity between clusters. This work is devoted to clustering time series. Various methods of time series clustering are considered. Examples are given for real data.

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Cited by 4 publications
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“…, J as a data matrix, where x ij represents the j-th variable observed for the i-th object. According to Kobylin and Lyashenko [36], the kmeans algorithm usually adopts the Euclidean distance as the proximity measure:…”
Section: Time Series K-means Clusteringmentioning
confidence: 99%
“…, J as a data matrix, where x ij represents the j-th variable observed for the i-th object. According to Kobylin and Lyashenko [36], the kmeans algorithm usually adopts the Euclidean distance as the proximity measure:…”
Section: Time Series K-means Clusteringmentioning
confidence: 99%