Abstract
Data Mining applications have found interesting applications in commercial and scientific domains. Last two decades have seen rapid strides in development of elegant algorithms that induce useful predictive and descriptive models from large data repositories available widely.
In last decade serious effort has also been made towards mining of evolving data-sets and now several one pass algorithms with restricted memory footprints are available for use in data stream environments. Study of temporal evolution of the patterns has been recognized as an important next generation data mining problem by both - the research and user communities. Comparative analyses of the changes detected in the discovered trends over the temporal dimension are likely to provide an insight into the dynamics of the dgp. Different levels of abstractions from the end-user’s viewpoint, form the second dimension of such analyses.
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Bhatnagar, V., Kochhar, S. (2009). Towards Characterization of the Data Generation Process. In: Nedjah, N., de Macedo Mourelle, L., Kacprzyk, J. (eds) Innovative Applications in Data Mining. Studies in Computational Intelligence, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88045-5_5
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DOI: https://doi.org/10.1007/978-3-540-88045-5_5
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