Title :
Comparing ODAC and Hierarchical algorithm using time series data streams
Author :
Kavitha, V. ; Punithavalli, M.
Author_Institution :
Dept of Comput. Sci., Karpagam Univ., Coimbatore, India
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.
Keywords :
data mining; electrocardiography; medical administrative data processing; pattern clustering; time series; ECG data set; ODAC algorithm; data clustering; data mining; hierarchical algorithm; online divisible agglomerative clustering algorithm; time series data stream; Clustering algorithms; Data mining; Electrocardiography; Indexes; Monitoring; Partitioning algorithms; Time series analysis; Data Streams; Hierarchical Clustering; Outliers;
Conference_Titel :
Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4244-5965-0
Electronic_ISBN :
978-1-4244-5967-4
DOI :
10.1109/ICCIC.2010.5705858
