Exploratory Data Mining

Catalog description

This course teaches fundamental concepts, algorithms and problem-solving techniques for exploratory data mining and their applications, with an emphasis on the algorithmic aspects. The course covers extensively the design of algorithms and implementations of software solutions to various exploratory data mining tasks. In addition, we discuss in depth various requirements and technical issues arising in the design and implementations of robust, efficient, effective and scalable software solutions. Topics covered include types and quality of data, similarity measures of data, algorithms and approaches for data processing/cleaning/analysis, data exploration, cluster analysis, association analysis, and anomaly detection. Additionally, basic concepts and techniques for predictive data mining, i.e., regression and classification, are reviewed. Students will exercise the obtained knowledge to address important technical issues in realistic exploratory data mining tasks and applications. Because the knowledge of various data structures (e.g., index structures, hash trees, graphs, lattices, and pointers) and algorithmic concepts (e.g., recursions, traversal, and pruning) are essential background to many data mining algorithms taught in the course. Students who are interested in the course are required to take the prerequisite courses in order to equip themselves with appropriate background knowledge and programming skillsets to succeed in the course.