By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)
This e-book constitutes the refereed complaints of the eleventh Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2007, held in Nanjing, China in may possibly 2007.
The 34 revised complete papers and ninety two revised brief papers awarded 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 functional improvement studies from all KDD-related components together with facts mining, computing device studying, databases, information, facts warehousing, info visualization, automated clinical discovery, wisdom acquisition and knowledge-based systems.
Read Online 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 resources for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings
Section 2 surveys related work. In section 3, we introduce the general idea for our method of classifier combination. Afterwards, section 4 describes methods to derive confidence ranges for various classifiers and explains their use for deriving confidence estimates. The results of our experimental evaluation are shown in section 5. Section 6 concludes the paper with a summary and ideas for future work. Multi-represented Classification Based on Confidence Estimation 25 2 Related Work In general, methods that employ multiple learners to solve a common classification problem are known as ensemble learning.
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. Furthermore, let d(x1 , x2 ) be the Euclidian distance in Rd . Then, CRangec,other (o) for a nearest neighbor classifier can be calculated as follows: CRangec,other (o) = d(uc , uother ) d(uc , uother )2 + d(uc , o)2 − d(uother , o)2 − 2d(uc , uother ) 2 A proof for this lemma can be found in . Unfortunately, CRangec,other (o) is much more complicated to calculate for k > 1 because this would require to calculate Voronoi cells of the order k.
11–22, 2007. c Springer-Verlag Berlin Heidelberg 2007 12 B. Andreopoulos, A. An, and X. Wang values resulting in a bad clustering), may return too many clusters or too many outliers, often have diﬃculty ﬁnding clusters within clusters or subspace clusters, or are sensitive to the order of object input [6,12,13,28]. We propose a categorical clustering algorithm that builds a hierarchy representing a dataset’s entire underlying cluster structure, minimizes user-speciﬁed parameters, and is insensitive to object ordering.