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Clustering Time Series using Unsupervised-Shapelets
| Citations: | 5 - 1 self |
BibTeX
@MISC{Zakaria_clusteringtime,
author = {Jesin Zakaria and Abdullah Mueen and Eamonn Keogh},
title = {Clustering Time Series using Unsupervised-Shapelets},
year = {}
}
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Abstract
Abstract — Time series clustering has become an increasingly important research topic over the past decade. Most existing methods for time series clustering rely on distances calculated from the entire raw data using the Euclidean distance or Dynamic Time Warping distance as the distance measure. However, the presence of significant noise, dropouts, or extraneous data can greatly limit the accuracy of clustering in this domain. Moreover, for most real world problems, we cannot expect objects from the same class to be equal in length. As a consequence, most work on time series clustering only considers the clustering of individual time series “behaviors,” e.g., individual heart beats or individual gait cycles, and contrives the time series in some way to make them all equal in length. However, contriving the data in such a way is often a harder problem than the clustering itself. In this work, we show that by using only some local patterns and deliberately ignoring the rest of the data, we can mitigate the above problems and cluster time series of different lengths, i.e., cluster one heartbeat with multiple heartbeats. To achieve this we exploit and extend a recently introduced concept in time series data mining called shapelets. Unlike existing work, our work demonstrates for the first time the unintuitive fact that shapelets can be learned from unlabeled time series. We show, with extensive empirical evaluation in diverse domains, that our method is more accurate than existing methods. Moreover, in addition to accurate clustering results, we show that our work also has the potential to give insights into the domains to which it is applied. Keywords-time series; clustering; shapelets; unsupervised I.
Keyphrases
time series extensive empirical evaluation important research topic individual gait cycle expect object local pattern dynamic time warping distance diverse domain keywords-time series multiple heartbeat abstract time series clustering individual time series behavior unlabeled time series significant noise real world problem entire raw data distance measure euclidean distance extraneous data individual heart first time unintuitive fact time series data mining past decade different length existing method clustering result
