Alternate Data Clustering for Fast Pattern Matching in Stream Time Series Data
Stream time series retrieval has been a major area of study due to its vast application in various fields like weather forecasting, multimedia data retrieval and huge data analysis. Presently, there is a demand for stream data processing, high speed searching and quick response. In this paper, the authors use a alternate data cluster or segment mean method for stream time series data, where the data is pruned with a computational cost of O (log w). This paper can be used for both static and dynamic stream data processing. The results obtained are the better than the existing algorithms.