Toward a MapReduce-Based K-Means Method for Multi-dimensional Time Serial Data Clustering

Abstract

Time series data is a sequence of real numbers that represent the measurements of a real variable at equal time intervals. There are some bottlenecks to process large scale data. In this paper, we firstly propose a K-means method for multi-dimensional time serial data clustering. As an improvement, MapReduce framework is used to implement this method in parallel. Different versions of k-means for several distance measures are compared, and the experiments show that MapReduce-based K-means has better speedup when the scale of data is larger.