Titlebook: Machine Learning and Data Mining in Pattern Recognition; 6th International Co Petra Perner Conference proceedings 2009 Springer-Verlag Berl

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Machine Learning and Data Mining in Pattern Recognition978-3-642-03070-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Discretization of Target Attributes for Subgroup Discovery the target data and uses them to select the discretization cutpoints. The algorithm has been implemented in a subgroup discovery method. Tests show that the discretization method likely leads to improved insight.

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Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Treesy detect the potential concept changes in the noisy data streams, but also performs much better on the abilities of runtime and space with an improvement in predictive accuracy. Thus, our proposed algorithm provides a significant reference to the classification for concept drifting data streams with noise in a light weight way.

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Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundanciesn of .. The proposed technique has been applied to a real case of analysis of textual data. An empirical comparison with the Apriori algorithm proves the advantages of the proposed method in terms of both time-performance and redundancy reduction.

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0302-9743 ous summits.Karl Marx A Universial Genius of the 19th CenturyMany scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern

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Selection of Subsets of Ordered Features in Machine Learningmputational complexity of such formulation. The effective method of solution is proposed. The brief survey of author’s early papers, the mathematical frameworks, and experimental results are provided.