Almost every computational method has been explored and used for financial modeling. However, how to apply the recent developed data mining theories to financial data mining; further how to deal with the large-scale financial data remains largely an open problem. Recently, more and more researchers in the community recognize the importance of the issue and seriously seek solutions. Unlike the other workshops which focus primarily on the algorithmic aspects, the intent of this workshop is to bridge the academia and industrial researchers to share their study and experience on financial data mining algorithms, theory and applications.

With the rapid globalization of the financial market, there has been an increasing demand for using data mining techniques in many core financial tasks, such as stock market forecasting, currency exchange rate, bank bankruptcies, financial risk management, credit rating, loan management, bank customer profiling, and money laundering. Nevertheless, the traditional data mining methods are far from practical uses for scenarios in financial data mining; it is not clear how the quickly emerging mining techniques can be used to improve the quality of financial data mining. Therefore, it is necessary to conduct a thorough investigation of the financial data mining problem and understand what the fundamental theoretical problem is in the financial mining.

In this workshop, we are interested in studying novel data mining technologies on financial data to make contributions for uncovering potential risks and predict future trends in financial markets. We are looking forward to high quality papers including theoretical research, empirical research and survey submissions. We will offer a chance for researchers and engineers to share information, their ideas and results on the latest explorations of FDM and forming collaborations for future works.

Topics of interest, but not limited to, are as follows:

• Data Mining in the current Financial Crises;
• Data Preprocessing in FDM;
• Association Rule based on Financial Data;
• Supervised Learning Models/Methods in FDM;
• Unsupervised Learning Models/Methods in FDM;
• Time Series Data Analysis;
• Outlier Detection in FDM;
• Neural Networks, Decision Tree and Support Vector Machine in FDM;
• Data Mining based Stock Price Forecasting Model;
• Data Mining based Finance Risks Forecasting Model;
• Data Mining based Finance Fraud Detection Model;
• Financial Privacy Preserving Data Mining;
• Matrix Factorization based Learning Model, including Principle Component Analysis, Singular Value Decomposition, Nonnegative Matrix Factorization, and Probabilistic Latent Semantic Indexing, in FDM;
• Text Mining in FDM
   

Wei Fan, Ph.D.
Research Staff Member, IBM T.J.Watson Research,
Title: Fast Real-time Fraud Modeling for Financial Services

Speaker Biography: Dr. Wei Fan received his PhD in Computer Science from Columbia University in 2001 and has been working in IBM T.J.Watson Research since 2000. He published more than 60 papers in top data mining, machine learning and database conferences, such as KDD, SDM, ICDM, ECML/PKDD, SIGMOD, VLDB, ICDE, AAAI, ICML etc. Dr. Fan has served as Area Chair, Senior PC of SIGKDD'06, SDM'08 and ICDM'08/09, sponsorship co-chair of SDM'09, award commitee member of ICDM'09, as well as PC of several prestigious conferences in the area including KDD'09/8/07/05, ICDM'07/06/05/04/03, SDM'09/07/06/05/04, CIKM'09/08/07/06, ECML/PKDD'07'06, ICDE'04, AAAI'07, PAKDD'09/08/07, EDBT'04, WWW'09/08/07, etc. He is on the advisory board of KD2U. Dr. Fan was invited to speak at ICMLA'06. He served as US NSF panelist in 2007/08. His main research interests and experiences are in various areas of data mining and database systems, such as, risk analysis, high performance computing, extremely skewed distribution, cost-sensitive learning, data streams, ensemble methods, easy-to-use nonparametric methods, graph mining, predictive feature discovery, feature selection, sample selection bias, transfer learning, novel applications and commercial data mining systems. He is particularly interested in simple, unconventional, but effective methods to solve difficult problems. His thesis work on intrusion detection has been licensed by a start-up company since 2001. His co-teamed submission that uses Random Decision Tree has won the ICDM'08 Contest Crown Awards. His co-authored paper in ICDM'06 that uses "Randomized Decision Tree" to predict skewed ozone days won the best application paper award. His co-authored paper in KDD'97 on distributed learning system "JAM" won the runner-up best application paper award.

• Submission Deadline: April, 20, 2009
• Notification of Acceptance: April, 29, 2009
• Workshop Date: July 2, 2009

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Proceedings for the workshops will be included in the proceedings of NISS 2009 and will be published by IEEE CS series, and indexed by EI.

For including your final paper to EI:

An abstract and index entry for each submission will be included within Elsevier's Ei Compendex database only if:

(1) the submission is available in full-text PDF format (multimedia submissions converted from formats such as Microsoft PowerPoint do not constitute full-text PDF format); and

(2) the submission includes an author-submitted abstract. If the submission is available in full-text PDF but does not include an abstract, only bibliographic information will be included in Ei Compendex. The foregoing conditions are subject to change based upon the terms and conditions of IEEE's agreement with Elsevier.

The papers submitted for review must be in the IEEE format (8.5" x 11", two-column) and not exceed 8 pages. Please strictly follow the formatting and layout instructions. The Submission Form is announced on the NISS'09 conference website. Authors should submit their papers by e-mail to the workshop organizer: zhyuanzh@gmail.com before April, 20, 2009. Papers will be selected according to their quality, significance, originality, and potential to generate discussion. Each paper will be reviewed by at least two referees from the workshop’s committee members.

• Yang Liu, Central University of Finance and Economics, China,
• Jie Tang, Tsinghua University, China,
• Ben-Chang Shia, Fu Jen Catholic University, Taiwan,
• Zhong-Yuan Zhang, Central University of Finance and Economics, China.

   

• Chris Ding, University of Texas at Arlington, USA,
• Hui Xiong, the State University of New Jersey, USA
• Chengzhang Wang, Central University of Finance and Economics, China,
• Keke Cai, IBM China Research Lab, China,
• Qinbao Song, Xi'an Jiaotong University, China,
• Ling Chen, University of Hannover, Germany,
• Zun-Quan Xia, Dalian University of Technology, China,
• Duo Zhang, University of Illinois at. Urbana-Champaign, USA,
• Limin Yao, University of Massachusetts Amherst, USA,
• Chuntao Li, Central University of Finance and Economics, China.