Titlebook: Research and Development in Knowledge Discovery and Data Mining; Second Pacific-Asia Xindong Wu,Ramamohanarao Kotagiri,Kevin B. Korb Confe

Hla461

Mining market basket data using share measures and characterized itemsets,(1) to present new itemset measures which are practical and useful alternatives to the commonly used support measure; (2) to not only discover the buying patterns of customers, but also to discover customer profiles by partitioning customers into distinct classes. We present a new algorithm for clas

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Automatic visualization method for visual data mining,nd rules from a large amount of data to support the decision making process. Although various approaches have been attempted in some research fields, we focused on the visual data mining support system from the viewpoint of harnessing the perceptual and cognitive capabilities of the human user. In v

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Identifying relevant databases for multidatabase mining, of databases, an immediate question facing practitioners is where we should start mining. In this paper, breaking away from the conventional data mining assumption that many databases be joined into one, we argue that the first step for multidatabase mining is to identify databases that are most li

是貪求

Minimum message length segmentation,nd image processing. In this paper, we consider a range of criteria which may be applied to determine if some data should be segmented into two or regions. We develop a information theoretic criterion (MML) for the segmentation of univariate data with Gaussian errors. We perform simulations comparin

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Conference proceedings 1998ne, Australia, in April 1998. The book presents 30 revised full papers selected from a total of 110 submissions; also included are 20 poster presentations. The papers contribute new results to all current aspects in knowledge discovery and data mining on the research level as well as on the level of

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Hybrid data mining systems: The next generation,s to the k-NN that make it appropriate for use as a paradigm for addressing regression data mining goals. We provide results obtained using these systems, comparing them with more traditional paradigms used to solve regression goals within Data Mining.

EXUDE

Single factor analysis in MML mixture modelling,lude an application of mixture modelling with single factors on spectral data from the Infrared Astronomical Satellite. Our model shows fewer unnecessary classes than that produced by AutoClass (Goebel et. al. 1989) due to the use of factors in modelling correlation.

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