Unsupervised learning of timeseries data is a challenging problem in machine learning. Here, we propose a novel algorithm, Deep Temporal Clustering (DTC), a fully unsupervised method, to naturally integrate dimensionality reduction and temporal clustering into a single end to end learning framework. The algorithm starts with an initial cluster estimates using an autoencoder for dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics are considered and compared. To gain insight into features that the network has learned for its clustering, we apply a visualization method that generates a heat map of regions of interest in the timeseries. The viability of the algorithm is demonstrated using timeseries data from diverse domains, ranging from earthquakes to sensor data from spacecraft. In each case, we show that our algorithm outperforms traditional methods. This performance is attributed to fully integrated temporal dimensionality reduction and clustering criterion.
Recent research in clustering methods for static data achieved superior performance by jointly optimizing a stacked autoencoder for dimensionality reduction and a k-means objective for clustering ( @cite_6 , @cite_1 ). However, those approaches were designed for static data, and are not well suited for time series data clustering.
@cite_1 @cite_6
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Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the well-known K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the sum of squared error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of-the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97 , which is an improvement of around 66 over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude. Hartigan’s method for k-means clustering is the following greedy heuristic: select a point, and optimally reassign it. This paper develops two other formulations of the heuristic, one leading to a number of consistency properties, the other showing that the data partition is always quite separated from the induced Voronoi partition. A characterization of the volume of this separation is provided. Empirical tests verify not only good optimization performance relative to Lloyd’s method, but also good running time. Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be protably modied to make use of this information. In experiments with articial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance. Study of this paper describes the incremental behaviours of partitioning based K-means clustering. This incremental clustering is designed using the cluster’s metadata captured from the K-Means results. Experimental studies shows that this clustering outperformed when the number of clusters increased, number of objects increased, length of the cluster radius decreased, while the incremental clustering outperformed when the number of new data objects are inserted into the existing database. In incremental approach, the K-means clustering algorithm is applied to a dynamic database where the data may be frequently updated. And this approach measure the new cluster centers by directly computes the new data from the means of the existing clusters instead of rerunning the K-means algorithm. Thus it describes, at what percent of delta change in the original database up to which incremental K-means clustering behaves better than actual K-means. It can be also used for large multidimensional dataset.