Abstract
Mining Time Series data has a remarkable development of concentration in today’s humanity. Clustering time series is a trouble that has applications in a wide variety of fields and has recently fascinated a huge amount of researchers. Time series data are frequently large and may surround outliers. In addition, time series are a special type of data set where elements have a chronological 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 extend an innovative method to solve the existing problem. This paper presents a new method called Fuzzy Hierarchical clustering algorithm is implemented. Experimental results on ECG data evaluate the processing qualities of the system is more accurate than the previous system.
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V.Kavitha, M.Punithavalli (2011). An Effective Performance of Fuzzy Hierarchical Clustering Using Time Series Data Streams. In: Das, V.V., Stephen, J., Chaba, Y. (eds) Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19542-6_39
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DOI: https://doi.org/10.1007/978-3-642-19542-6_39
Publisher Name: Springer, Berlin, Heidelberg
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