Comparing ODAC and Hierarchical algorithm using time series data streams

Abstract

Mining Time Series data has a tremendous growth of interest in today's world. Clustering time series is a trouble that has applications in an extensive assortment of fields and has recently attracted a large amount of researchers. Time series data are frequently large and may contain outliers. In addition, time series are a special type of data set where elements have a temporal ordering. Therefore clustering of such data stream is an important issue in the data mining process. The clustering algorithms and its effectiveness on various applications are compared to develop a new method to solve the existing problem. This paper presents a comparison between Hierarchical clustering algorithm and Online Divisible Agglomerative Clustering algorithm (ODAC) using ECG data sets. © 2010 IEEE.