By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)
This publication constitutes the refereed lawsuits of the eleventh Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2007, held in Nanjing, China in may perhaps 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 useful improvement reports from all KDD-related parts together with info mining, desktop studying, databases, records, info warehousing, information 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 info for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings
Insert the ﬁrst object into a new cluster, use the object as the mode of the cluster, and remove the object from S. 3. Initialize φ to 1. 4. Loop through the following until S is empty or φ > threshold a. For each object o in S i. Find o’s nearest cluster c by using the dissimilarity metric to compare o with the modes of all existing cluster(s). ii. If the number of diﬀerent values between o and c’s mode is larger than φ, insert o into a new cluster iii. Otherwise, insert o into c and update c’s mode.
Fi denotes the corresponding feature space of representation i and ”−” denotes that there is no object 26 J. Aßfalg et al. description for this representation. Missing representations are a quite common problem in many application areas and thus, should be considered when building a method. Obviously, for each object there has to be at least one oj = ”−”. For a given set of classes C = ci , . . , ck , our classification task can be described in the following way. Given a training set of multi-represented objects T R ⊂ R1 × .
De Abstract. Complex objects are often described by multiple representations modeling various aspects and using various feature transformations. To integrate all information into classification, the common way is to train a classifier on each representation and combine the results based on the local class probabilities. In this paper, we derive so-called confidence estimates for each of the classifiers reflecting the correctness of the local class prediction and use the prediction having the maximum confidence value.
Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings by Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)