A Distributed Information Granulation Method for Time Series Clustering

Abstract

Time series clustering is an important research problem in machine learning and data mining. With the rapid increase in the amount of time series data, many traditional clustering algorithms cannot directly deal with large-scale time series due to some limitations in the memory capacity and the execution time. In this study, we suggest a distributed information granulation method for large-scale time clustering problem. First, a distributed time series partitioning method is designed to randomly divide the original time series dataset into some data blocks. Then, the distributed time series granulation method is developed in the map-reduce framework by the principle of reasonable granularity, where each time series can be described by some representative data points to show the trend state information. Finally, we introduce the large-scale time series clustering method in terms of the fuzzy C-means clustering algorithm. The experimental studies demonstrate the feasibility and the effectiveness on several UCR publicly benchmark time series datasets. Compared with the classical clustering methods, the proposed method can achieve a 4.86–9.65% improvement in average clustering accuracy. Meanwhile, the proposed method exhibits more advantages in both unequal length time series clustering and execution time.