Exploration and Performance Analysis of Clustering Algorithms for Time-Series Data with Dimension Reduction

Clustering is an attempt to form groups of similar objects, and it is a powerful tool for discovering valuable underlying patterns in the data. When clustering on high dimensional data, the algorithms can suffer from the curse of dimensionality. This is a problem that occurs when data becomes sparse due to many dimensions, and can lead to poor clustering performance. Dimensionality reduction methods (DRMs) are thus designed to help alleviate this issue. For a time-series that is a temporal set of points, each consecutive point in time can be considered a dimension and therefore it belongs to high dimensional data. Time-Series K-Means (TSK-Means) with Dynamic Time Warping (DTW) is an algorit…