Titlebook: Advances in Knowledge Discovery and Data Mining; 11th Pacific-Asia Co Zhi-Hua Zhou,Hang Li,Qiang Yang Conference proceedings 2007 Springer-

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The Importance of Being Siblings for unsupervised image segmentation based on the finite mixture model, which can make automatic model selection through introducing entropy regularization into maximum likelihood (ML) estimation. Some segmentation experiments on the Corel image database further demonstrate that the iterative ERL le

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Imaging Management and Integration,ions in data mining and discuss some recent progress in this direction, including (1) pattern mining, usage, and understanding, (2) information network analysis, (3) stream data mining, (4) mining moving object data, RFID data, and data from sensor networks, (5) spatiotemporal and multimedia data mi

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HCUG Clinical Information System,identification, noise profiling, and noise tolerant mining. During noise identification, erroneous data records are identified and ranked according to their impact or some predefined measures. Class noise and attribute noise can be distinguished at this stage. This identification allows the users to

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The Resource Management Component,ropose the HIERDENC algorithm for .. HIERDENC offers a basis for designing simpler clustering algorithms that balance the tradeoff of accuracy and speed. The characteristics of HIERDENC include: . it builds a hierarchy representing the underlying cluster structure of the categorical dataset, . it mi

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Overview of Hardware and Softwarend give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A .framework is proposed to improve mining efficiency. Through extensive ex

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https://doi.org/10.1007/978-1-4613-8593-6page clustering, different algorithms produce clusterings with different characteristics: coarse vs fine granularity, disjoint vs overlapping, flat vs hierarchical. The lack of a clustering evaluation method that can evaluate clusterings with different characteristics has led to incomparable researc