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In this paper we propose to process big data using a data streams approach. The data set is divided into subsets, each subsets is considered as a time window ...
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This paper is the first to investigate the use of data stream clustering algorithms as light- weight alternatives to conventional algorithms on large non- ...
Mar 7, 2024 · A very common approach to data stream clustering is to cluster data in two phases: online and offline. It was mentioned that the data stream ...
Dec 1, 2016 · The data stream clustering problem requires a process capable of partitioning observations continuously while taking into account restrictions ...
The proposed method consists of three phases: data initialization, online clustering, offline clustering. Initially, the input data are taken from Forest Cover ...
Jul 13, 2020 · Data stream clustering refers to the clustering of data that arrives continually such as financial transactions, multimedia data, or telephonic records.
The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream.
In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art ...
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In this paper we propose an algorithm for online clustering of data streams. This algorithm is called AutoCloud and is based on the recently introduced concept ...
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Jan 14, 2003 · The stream model is motivated by emerging applications involving massive data sets; for example, customer click streams, telephone records ...
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