Time series clustering techniques
As a form of unsupervised learning, clustering approaches enable associations between time series to be identified without being subjected to human grouping biases. However, in order to apply clustering techniques it is necessary to be able to describe the relationships between successive pairs of time series using single value representations. Consequently time series clustering techniques tend to be based on measures of the relative distance between curves, rather than the curve data points themselves. There is also considerable variation in the outcomes depending on the clustering algorithm selected for use. This can be in terms of the real-world interpretation of the groupings generated, as observed when comparing clusters predicted using the K-means and K-medoids algorithms. Fig. \ref{943632} below illustrates how the centre of subsets in K-means is equivalent to the mean of measurements in the subset (the centroid), rather than an actual member of the subset (a medoid). As such K-means is not appropriate for application to time series, as the algorithm ends up minimising variance, rather than distances between curves \cite{k-medoids_clustering,Dynamic_Time_Warping_Clustering}.