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Propose an algorithm to detect concept drift and simplified fading function for removing expired micro-clusters and detect novel micro-clusters and outliers in batch processing. .
As mentioned, after each window time, novel micro-clusters and outliers are distinguishable.
How is the quality of DCSTREAM in detecting novel micro-clusters and outliers? 5.
To facilitate distinguish between the novel micro-clusters and abnormalities; we will introduce a parameter which is called as outlier factor and defined by the user.
ConStream uses a method during the clustering to detect novel micro-clusters and outliers which is time consuming (trend setter and mature cluster) because of applying fading function during the stream clustering [27].
In contrast, DCSTREAM detect novel micro-clusters and outliers after each stream processing automatically and uses fading function only for expired micro-clusters to remove from the active memory.
Every macro-cluster can include more than one micro-cluster and yields a hierarchical structure for clustering.
Micro clusters decay function and deletion mechanism of micro clusters are used to maintain the micro clusters, which reflects the data stream evolution process accurately.
So, Raman spectroscopy confirms the information on the presence of nano and micro clusters of crystal silicon in various surroundings and its size dispersion [20].
2) Micro clusters are obtained to reveal local clusters and, hence, further partition of the data set is avoided.
Micro clusters.
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