Application of agglomerative hierarchical clustering for clustering of time series data

Abstract

The advancements in technology have made it possible to automatically record and store large amount of data, which has resulted in a need for development and application of efficient data analysis techniques. Unsupervised data clustering methods have proven to be capable of extracting useful information from various types and sizes of datasets. This paper investigates the performance of the standard agglomerative hierarchical clustering algorithm using two time series datasets from electric power system and neuroscience area. The main steps in clustering procedure are presented in detail. Results show that the effectiveness of the clustering algorithm is affected to a large extent by the main characteristics of the clustering data and algorithm's parameters.