By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)
This ebook constitutes the refereed complaints of the eleventh Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2007, held in Nanjing, China in could 2007.
The 34 revised complete papers and ninety two revised brief papers offered including 4 keynote talks or prolonged abstracts thereof have been conscientiously reviewed and chosen from 730 submissions. The papers are dedicated to new principles, unique examine effects and sensible improvement studies from all KDD-related parts together with facts mining, desktop studying, databases, records, info warehousing, facts visualization, computerized medical discovery, wisdom acquisition and knowledge-based systems.
Read or Download Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings PDF
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Additional info for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings
In this case, we can easily Multi-represented Classification Based on Confidence Estimation 29 calculate CRange(o) on the basis of the particular CRangecpred,cother (o). Thus, we only need to compare the distances to the class border which is determined by the nearest neighbor uc of the predicted class c to any nearest neighbor ucˆ of the other classes cˆ. This distance can be calculated using the following lemma. Lemma 1. Let o be an object, let uc be the nearest neighbor belonging to class CL(o) = c and let uother be the nearest object belonging to some other class other ∈ C \ c.
Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae. Genome Res. 13, 2450-4, 2003 8. M. P. Kriegel, J. Sander, X. Xu. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996 9. G. Even, J. Naor, S. Rao, B. Schieber. Fast Approximate Graph Partitioning Algorithms. SIAM Journal on Computing, 28(6):2187-2214, 1999 22 B. Andreopoulos, A. An, and X. Wang 10. V. Ganti, J. Gehrke, R. Ramakrishnan. CACTUS-clustering categorical data using summaries.
The leaf clusters created for r ≤ 9 are homogeneous with regards to the class labels of member objects. For leaf clusters created for r > 9, the homogeneity of the class labels decreases. Only 23 objects are clustered for r > 9, so these could be labeled as outliers. For soybean-data we cut oﬀ the HIERDENC tree at r = 4; soybean-data is a sparse cube of mostly ‘0’ cells, since the dataset has 35 dimensions but only 307 objects. The r = 4 cut-oﬀ minimizes the connectivity relative to r of the resulting clusters.