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Title: Introduction to Data Mining

Speaker: Koh Hian Chye

Biography:
Dr Koh Hian Chye is an Associate Professor and Dean of the School of Business at SIM University (UniSIM). He has published widely in international and regional journals and conferences on topics related to accounting and auditing, business and management, statistical modelling and data mining. His current interest is in the business applications of data mining. Dr Koh frequently acts as a consultant to private companies, large organisations and government agencies. His recent consultancy projects involve data mining applications. He also serves as a trainer in workshops and executive programmes. Dr Koh has recently written a book entitled "Data Mining Applications for Small and Medium Enterprises", which is available at branches of the National Library in Singapore.

Title: Advanced Topic I: Anomaly detection and discovery

Speaker: David Hand

Abstract:
A major aim of data mining is to detect data configurations representing real underlying peculiarities of the phenomenon under investigation. This is an important activity in both science and commerce. In science, it enables one to detect when a theory does not adequately explain the data, and so needs replacing or extending. In commerce it enables one to identify unusual features of customer behaviour. This talk examines the theory, methods, and tools of anomaly detection data mining. Examples are given, and challenges are described.

Biography:
David Hand is Professor of Statistics and Head of the Statistics Section at Imperial College London. He has published over twenty books on statistics and related areas, including Principles of Data Mining. He launched the journal Statistics and Computing, and served a term of office as editor of Journal of the Royal Statistical Society, Series C. He was awarded the Thomas L. Saaty Prize for Applied Advances in the Mathematical and Management Sciences in 2001, the Royal Statistical Society's Guy Medal in Silver in 2002, the IEEE International Conference on Data Mining award for Outstanding Contributions in 2004, and was elected Fellow of the British Academy in 2003. He acts as a consultant to a wide range of organisations, including governments, banks, pharmaceutical companies, manufacturing industry, and health service providers.

Title: Advanced Topic II: Privacy Preservation in Data Mining

Speaker: Wang Ke

Abstract:
Data mining enables us to analyze corporate data to find ways to increase efficiency of an organization. This technology may present a threat to the privacy rights of individuals when used to infer “private" information from “public" data. For example, under the HIPAA Privacy Rule, genomic data such as person-specific DNA are not specified as an identifying patient attribute. As such, genomic data may be released for public research purposes. Though genomic data alone is not sensitive, its associated information, such as Gender, Zip Code and Birthdate, may be used to link genomic data to explicit identity of contributors, which violates the privacy rights. In other cases, data mining involves data owned by several parties and collecting all private data in one place is impractical. On the other hand, the patterns and trends which data mining seeks to extract should not depend on sensitive information that is usually specific to individuals. Often, it is the access to the data, before data mining starts, that causes the privacy concerns. An interesting question is how data can be rendered so that sensitive information is masked and usefulness to data mining is retained. This talk will examine these issues. Examples will be used to illustrate problems and solutions.

Biography:
Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught in the areas of database and data mining.

Ke Wang's research interests include database technology, data mining and knowledge discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real life problems.
Ke Wang has published in database, information retrieval, and data mining conferences, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM and ICDM. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences including DASFAA, ICDE, ICDM, PAKDD, PKDD, SIGKDD and VLDB.

Title: Advanced Topic III: Data Mining for Customer Relationship Management Applications

Speaker: Jaideep Srivastava

Abstract:
Corporations across the world are recognizing that intimate, one-to-one relationships with their customers are critical for survival in the increasingly global and competitive marketplace. The ones which are proactive and quick footed, have taken the initiative to implement a Customer Relationship Management (CRM) system that integrates every area of business that touches the customer - namely marketing, sales, and customer service - by coordinating people, internal processes and technology. A traditional CRM system typically focuses on reengineering the transactions and workflows to make them customer centric, however to gain competitive advantage it is equally important to analyze the business data for locating patterns in customer behavior that would help in customer acquisition, retention, and building customer loyalty. This can be achieved by coupling Data Analytics with traditional CRM.

The tremendous leaps in storage and computational power have made Data Analytics emerge as a powerful business tool that unleashes the power in your data across the organization for better decision making. Data Analytics combines data warehousing, data mining and mathematical modeling concepts to decipher previously unknown, actionable information from business data. Because the basis of data analytics is data - the facts about what has already happened in the organization - data analytics enables the organization to leverage the experience to make better decisions today. This talk provides an up-to-date introduction to the increasingly important field of "Analytical CRM", whose goal is provide a quantitative basis for making CRM decisions – thus leading the transition from customer relationship as an art to a science. Case studies from Amazon.com and Yodlee will be presented to illustrate the concepts.

Biography:
Jaideep Srivastava has established and led a laboratory that has conducted research in databases, multimedia systems, and data mining over the past 17 years. He has supervised 23 Ph.D. dissertations and 44 MS theses, and has authored/co-authored over 180 papers in journals and conferences. He received a Best Paper award for his joint work on applying competitive economics models to the problem of Quality-of-Service based resource scheduling in distributed multimedia systems, in 1999. Dr. Srivastava has active collaboration with the technology industry, both for research and technology transfer. He has chaired/co- chaired a number of conferences, and is on the editorial board of many journals, including IEEE TKDE, IEEE TPDS, the VLDB Journal, the WWW Journal, and the KAIS Journal. An often-invited participant in technical and technology strategy forums, Dr. Srivastava is a popular speaker at industry, academic and government meetings. The US federal government has solicited his opinion on computer science research as an expert witness. Dr. Srivastava's industry experience includes heading data mining at Amazon.com, and data warehousing, mining, and reporting at Yodlee. He has provided technology and technology strategy advice to a number of large corporations, including Cargill, United Technologies, IBM, Honeywell, 3M, and Persistent Systems. He has served in an advisory capacity to a number of small companies, including Lancet Software, and Infobionics. A unique aspect of Dr. Srivastava's career is that he is equally at ease in the academia as in industry, and thus is able to see things from both perspectives. In recognition of this, he has been invited to be the Conference Chair for SAS Inc's Data Mining Tehcnology Summitt (the premier industry forum for data mining), and as Technical Chair of ACM SIGKDD's Industrial Program Committee. He has also served as general chair and program chair for many academic research conferences. He has been elected a Fellow of the IEEE, and has been appointed a Distinguished Visitor by the IEEE Computer Society. He is on the High Technology Advisory Council to the Chief Minister of Maharashtra, India.