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Data streams are one of the most challenging environments for machine learning. In many applications, the high volume data streams have an inherent concept drift over time. Identifying novel classes and detecting the occurrence of concept drift in such an environment is a major challenge. In this paper, a new method has been proposed to detect novelty and handle concept drift with limited required...
We present an algorithm called HS-means which is able to learn the number of clusters in a mixture model. Our method extends the concept of clustering stability to a concept of hierarchical stability. The method chooses a model for the data based on analysis of clustering stability; it then analyzes the stability of each component in the estimated model and chooses a stable model for this component...
The slashing is a very important procedure in textile manufacturing process which can improve warp quality, loom efficiency and reduce warp break. A hybrid modeling method is proposed for textile slashing process. Data are divided to multiple subsets by clustering algorithm, and then artificial neural networks (ANN) and partial least square (PLS) regression are used to model multiple sub-models respectively...
Recently, data mining over uncertain data streams has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications. In this paper, we try to resolve the problem of clustering over uncertain data streams. Facing uncertain tuples with different probability distributions, the clustering algorithm should not only consider the tuple value...
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