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ALSIP '08
WMWA '08
DMDRM '08
IDM '08
NTMD '08   canceled
 
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Organized by:

  

I.S.I.R., Osaka University
 
Co-organized by:


School of Science & Technology, Kwansei Gakuin University


Faculty of Commerce, Kansai University

 
In Cooperation with:



The Japanese Society of Artificial Intelligence
 

 

 


Tutorials

As for all previous PAKDD conferences this year again we organised a tutorial program designed to provide attendees an overview of the state-of-the-art and practical applications in important current areas in Knowledge Discovery and Data Mining.

Due to space limitations we could only select three from the many submitted high quality tutorial proposals which came all from distinguished researchers in their respective field.

Each tutorial will run for three hours, in the morning of 20 May 2008, or in the afternoon. The topics are on Data Stream Mining, Detecting Clusters in Moderate to High Dimensional Data, and Web Spam Detection. These were selected in order to offer the attendees of this year's PAKDD overviews of a broad spectrum of important current and emerging research and application areas. We believe attendees will be able to significantly benefit from the tutorials on offer.
 

Prof. Joao Gama

Laboratory of Artificial Intelligence and Decision Support,
INESC-Porto, University of Porto, Portugal

Homepage: http://www.liaad.up.pt/~jgama/


Dr. Mohamed Medhat Gaber

Tasmanian ICT Centre, CSIRO ICT Centre, Australia

Homepage: http://www.geocities.com/medhatgaber/

Title:

State-of-the-art in Data Stream Mining

 

Abstract:

Data streams became ubiquitous as many sources produce data continuously and rapidly. Examples of streaming data include sensor networks, customer click streams, telephone records, web logs, multimedia data, sets of retail chain transactions, etc. These data sources are characterized by continuous generating huge amounts of data from non stationary distributions. Data Streams have brought new challenges to the data mining research community. In consequence, new techniques are needed to process streaming data in reasonable time and space. The goal of this tutorial is to present and discuss the research problems, issues and challenges in learning from data streams. We will present the state-of-the-art techniques in change detection, clustering, classification, frequent patterns, and time series analysis from data streams. Applications of mining data streams in different domains are highlighted. Open issues and future directions will conclude this tutorial. The tutorial also points to data stream mining resources.
 

Short Biography:
Joao Gama is a researcher at LIACC, the Laboratory of Artificial Intelligence and Computer Science of the University of Porto, working at the Machine Learning group. His main research interest is in Learning from Data Streams. He has published several articles in change detection, learning decision trees from data streams, hierarchical Clustering from streams, etc. Editor of special issues on Data Streams in Intelligent Data Analysis, J. Universal Computer Science, and New Generation Computing Co-chair of a series of Workshops on Knowledge Discovery in Data Streams, ECML 2004, Pisa, Italy, ECML 2005, Porto, Portugal, ICML 2006, Pittsburg, US, ECML 2006 Berlin, Germany, SAC2007, Korea, and the ACM Workshop on Knowledge Discovery from Sensor Data to be held in conjunction with ACM SIGKDD 2007. Gama and Gaber edited the book: Learning from Data Streams-Processing Techniques in Sensor Networks, published by Springer.

Mohamed Medhat Gaber is a research scientist at Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia. He has published more than 40 articles. Mohamed has served in the program committees of several international and local conferences and workshops in the area of data mining. He has also been serving as a reviewer for the special issues of international journals in the area of data stream mining. He was the co-chair of the International Workshop on Mining Evolving and Streaming Data held in conjunction with ICDM 2006. He is the co-chair of the International Workshop on Knowledge Discovery from Ubiquitous Data Streams to be held in conjunction with ECML/PKDD 2007 and the ACM Workshop on Knowledge Discovery from Sensor Data to be held in conjunction with ACM SIGKDD 2007. Gama and Gaber edited the book: Learning from Data Streams-Processing Techniques in Sensor Networks, published by Springer.
 

 

Prof. Hans-Peter Kriegel

Database and Information Systems, Institute for Informatics, University of Munich, Germany

Homepage: http://www.dbs.informatik.uni-muenchen.de/Mitarbeiter/kriegel.html


Dr. Peer Kröger

Database and Information Systems, Institute for Informatics, University of Munich, Germany

Homepage: http://www.dbs.informatik.uni-muenchen.de/Mitarbeiter/kroegerp.html


Mr. Arthur Zimek

Database and Information Systems, Institute for Informatics, University of Munich, Germany

Homepage: http://www.dbs.informatik.uni-muenchen.de/Mitarbeiter/zimek.html

Title:

Detecting Clusters in Moderate-to-High Dimensional Data Subspace Clustering, Pattern-based Clustering, and Correlation Clustering

 

Abstract:

This tutorial provides a comprehensive and comparative overview of a broad range of state-of-the-art algorithms for finding clusters in moderate-to-high-dimensional data. It sketches important applications of the introduced methods, outlines the general challenges these algorithms have to cope with, and presents a taxonomy of existing approaches. In addition, relationships between the algorithmic approaches of each category of the taxonomy are discussed. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with high-dimensional data.
 

Short Biography:
(to be announced)Hans-Peter Kriegel is a full professor for database systems and data mining in the Department ``Institute for Informatics'' at the Ludwig-Maximilians-Universitaet Muenchen, Germany and has served as the department chair or vice chair over the last years. His research interests are in spatial and multimedia database systems, particularly in query processing, performance issues, similarity search, high-dimensional indexing as well as in knowledge discovery and data mining. Kriegel received his MS and Ph.D. in 1973 and 1976, respectively, from the University of Karlsruhe, Germany. Hans-Peter Kriegel has been chairman and program committee member in many international database and data mining conferences. He has published over 200 refereed conference and journal papers, and he received the ``SIGMOD Best Paper Award'' 1997 and the ``DASFAA Best Paper Award'' 2006 together with members of his research team.
 

Peer Kroeger is an assistant professor at the Ludwig-Maximilians-Universitaet Muenchen, Germany. He finished his PhD thesis on clustering moderate-to-high dimensional data in summer 2004 and is currently working towards his Habilitation with research interests in data mining and similarity search in high dimensional multimedia and biomedical data. Several of his recent publications include contributions to the tutorial's scope.

 

Arthur Zimek is a PhD student in the database and data mining group of Hans-Peter Kriegel at the Ludwig-Maximilians-Universitaet Muenchen, Germany. His research interests include data mining for high dimensional data and structured data especially for bioinformatics applications. Several of his recent publications include contributions to the tutorial's scope.
 


 

Prof. Jian Pei

School of Computing Science
Simon Fraser University, Canada

Homepage: http://www.cs.sfu.ca/~jpei/


Mr. Bin Zhou

School of Computing Science
Simon Fraser University, Canada

Homepage: http://www.cs.sfu.ca/~bzhou/personal/


Dr. ZhaoHui Tang

Microsoft, U.S.A.

 


Mr. Dylan Hai Huang

Microsoft, U.S.A.

 

Title:

Data Mining Techniques for Web Spam Detection

 

Abstract:

Web spam, which refers to any deliberate actions bringing to selected web pages an unjustifiable favorable relevance or importance, hurts the quality of information retrieval on the web. The tricks of web spam can be roughly divided into two categories: content spam and link spam. Content spam tricks disguise the content of a web page so that it appears relevant to many popular searches. Link spam tricks boost page rankings by making up some linkage structures. Interdisciplinary data mining techniques have been extensively used to combat web spam. However, as web spam tricks become more and more sophisticated, effective and efficient data mining techniques dedicated for web spam detection are critical, and core data miners are highly wanted. It is high time the data mining community seriously addressed the demand of data mining techniques for spam detection. In this tutorial, we will elaborate some major ideas in spam detection and review the major data mining techniques including supervised methods and unsupervised methods for spam detection. We will also discuss the spam issue and the detection techniques in sponsored search and adversarial information retrieval. Lastly, we will analyze the challenges and opportunities for data mining community.
 

Short Biography:
Jian Pei received his Ph.D. in Computing Science from Simon Fraser University, Canada, in 2002. He is currently an Assistant Professor of Computing Science at Simon Fraser University, Canada. His research interests can be summarized as developing effective and efficient data analysis techniques for novel data intensive applications. Currently, he is interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in web search, sensor networks, bioinformatics, privacy preservation, and education. His current research has been supported in part by NSERC, NSF, Microsoft, IBM, HP, CIBC, Michael Smith Foundation, and the SFU Community Trust Endowment Fund. He has published prolifically in refereed journals, conferences, and workshops. He is an associate editor of IEEE Transactions on Knowledge and Data Engineering. He has served regularly in the organization committees and the program committees of many international conferences and workshops, and has also been a reviewer for the leading academic journals in his fields. He is senior members of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is the recipient of the British Columbia Innovation Council 2005 Young Innovator Award.

Bin Zhou is a Ph.D. student in School of Computing Science at Simon Fraser University, Canada. His current research interests are in database, data mining and information Retrieval, particularly in data mining and privacy preserving techniques for social networks.

ZhaoHui Tang is a group program manager at Microsoft adCenter Labs, where he manages a number of research projects related to paid search and content advertisements. He is the main inventor of Microsoft Keyword Services Platform -- the first web service platform specialized on keyword technologies in the industry. Prior to adCenter, he spent 6 years as a lead program manager in SQL Server Business Intelligence group, mainly focusing on data mining development. ZhaoHui Tang got his Ph.D. in France in 1996 on the topic of database query processing. Prior joining Microsoft, he worked in Sema Group based in Paris, leading an R\&D team doing data mining project. He has numerous publications in both academic and industrial journals such as VLDB and SQL Server Magazine. He is a frequent speaker in database and business intelligence conferences and often serves as committee members for those conferences. He holds a guest professorship in Nan Kai University, China. He is a co-author of the book "Data Mining with SQL Server 2005".

Dylan Hai Huang is a Program Manager in Microsoft adCenter Labs. Currently, he is working on Microsoft Keyword Services Platform (KSP) and its related applications. KSP packaged many advanced research algorithms to a development platform and has been widely adopted in search engine marketing and optimization community. He is pursuing his Ph.D degree in Computing Science in Simon Fraser University. Before joining adCenter Labs, he spent over 5 years in Microsoft Business Intelligence groups, working on Microsoft Analysis Services OLAP related technologies. He coauthored the book "MDX Solutions: With Microsoft SQL Server Analysis Services 2005 and Hyperion Essbase". His major research interests are data mining and machine learning.