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A Framework of Multi Instance Objects for Resourceful Retrieval of Specific Websites

M. Jenifer, Dr. S. Thabasu Kannan


Knowledge Discovery in Database (KDD) is the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in large data collections. However, with the growing amount of data the complexity of data objects increases as well. Multi-instance and multi-represented objects are two important types of object representation for complex objects. The main contribution of this proposed work is the development of new KDD methods for the classification and clustering of complex objects. Therefore, the proposed work introduces solutions for real world applications that are based on multi instance object representations. On the basis of these solutions, it will show that a more general object representation often provides better results for many relevant KDD applications. The first is the data mining and its uses of projected clustering for the automatic construction of product projections. The introduced solution decomposes a single part into a set of subspaces and compares them by using a metric on multi-instance of objects.


Knowledge Discovery in Database (KDD); Multi-Instance and Multi-Represented Objects; Projected Clustering

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