By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)

This ebook constitutes the refereed court cases of the eleventh Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2007, held in Nanjing, China in might 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 study effects and functional improvement reviews from all KDD-related components together with info mining, laptop studying, databases, information, information warehousing, information visualization, automated medical discovery, wisdom acquisition and knowledge-based systems.

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Extra resources for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings

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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.

Zaki, M. Peters. CLICK: Clustering Categorical Data using K-partite Maximal Cliques. TR04-11, Rensselaer Polytechnic Institute, 2004 29. Y. W. H. A. Heng. Clustering Categorical Data. 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.

Multi-modal and Multi-granular Learning∗ Bo Zhang1 and Ling Zhang2 1 Dept. cn Introduction Under large scale data, machine learning becomes inefficient. In order to enhance its performances, new methodologies should be adopted. Taking video retrieval as an example, we discuss its two basic problems: representation and classification problems. In spite of the form (text, image speech, or video) of information there always exists a big semantic gap between its low-level feature based machine representations and the corresponding high-level concepts used by users, so the traditional single feature machine representation is not available under large scale data.

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