NEW !!!

 

 

 

 

Workshops - Monday April 16, 2001

Three workshops will be held in conjunction of PAKDD 2001 on April 16, 2001.

Workshop on Mining Spatial and Temporal Data

April 16, 2:00-5:00pm

Objectives

In recent years, we witness a rapid growth of interest in spatial and/or temporal data and its applications. We believe that it is an important challenge to extend data mining algorithms to work with spatial and temporal data. This workshop aims to bring together researchers from academia and industry who are working on spatial and/or temporal data mining areas to discuss recent developments and explore new approaches and issues. This workshop will foster the discussion of research works and directions through paper presentation, panel discussion, and invited talks.

 

Workshop on Statistical Techniques in Data Mining with Applications

April 16, 9:00am-12:00pm

Objectives

Data mining utilizes advances in both Computer Science and Statistics to extract unexpected or meaningful structures from huge data. Statistical methods are thus used in many data mining problems, particularly in the areas of model building and pattern detection that capture key relationships between variables in the data. Other statistical methods often used in data mining include data cleaning, sample selection, model selection and time series models. Recently, many popular statistical packages developed data mining tools that facilitate the use of statistical methods in data mining.

This workshop aims to bring together statisticians and practitioners in data mining to promote a more active interaction among disciplines. It allows for a comprehensive presentation of various relevant topics, and introduces recent statistical applications in data mining. Bring different research communities together and providing a better understanding of the statistical underpinnings for other disciplines are two key ingredients to the quest for better ways of solving various data mining problems. The workshop will serve as a forum where the statistical methods used in data mining are investigated. We believe more research and application activities could then be stimulated.