Knowledge discovery and data mining (KDD) is a multidisciplinary effort to mine gold nuggets of knowledge from data. The increasingly large data sets from many application domains have posed unprecedented challenges to KDD; in the meantime, new types of data are evolving such as social media, text, and microarray data, to name a few. Researchers and practitioners in multiple disciplines and various IT sectors confront similar issues in feature selection, and there is a pressing need for continued exchange and discussion of challenges and ideas, exploring new methodologies and innovative approaches to generate breakthroughs.
Feature selection is effective in data preprocessing and reduction that is an essential step in successful data mining applications. Feature selection has been a research topic with practical significance in many areas such as statistics, pattern recognition, machine learning, and data mining (including Web, text, image, and microarrays). The objectives of feature selection include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data.
Workshop on Feature Selection in Data Mining (FSDM2010) aims to further the cross-discipline, collaborative effort in variable and feature selection research. FSDM2010 will be held at the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2010)