Clustering Process with Time Series Data Stream

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Abstract

Data stream is an ordered sequence of data objects that can be read only once or a small number of times. The characteristics of data stream are very large, continuous, high dimensional, immeasurable, dynamically high speed, and massive amount of data in offline and also in online, and there is not sufficient time to rescan the entire database. Data stream are required to store vast amounts of data that are continuously inserted and queried. Due to the above features of data stream, obtaining the fruitful information is a critical process. Hence, analyzing huge data sets and extracting valuable pattern in many applications is interesting for researchers.

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Kavitha, V., Hemashree, P., Dilip, H., & Elakkiyarasi, K. (2020). Clustering Process with Time Series Data Stream. In Smart Innovation, Systems and Technologies (Vol. 159, pp. 335–343). Springer. https://doi.org/10.1007/978-981-13-9282-5_31

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