Intelligent Information Systems (IIS) Lab
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  • SVM-JAVA: A Java implementation of the SMO (Sequential Minimal Optimization) for training SVM

  • Ranking Vector SVM (RV-SVM): An efficient 1-norm-based method for learning ranking SVM (need CPLEX)

  • VRIFA: A nonlinear SVM visualization tool using nomogram and localized radial basis function (LRBF) kernels

  • RankRelief: An efficient feature weighting method for ranking
Our research interests are in the area of data mining. Data is everywhere, heterogeneous, and continuously increasing. For example, rapidly growing are Web data on the Internet, commercial data warehouses, biological data, scientific data, etc. Mining or extracting knowledge from such massive data is important but challenging: rich data but poor information is a common phenomenon in the real world. For this reason, data mining has been extensively exploited for the last decade. It has applications in various domains and is still a largely open area. Our goal is to develop and advance technologies in data mining, and contribute to the communities that are in need of extracting knowledge from data.

Our current research problems include collaborative filtering and recommending systems, mining text and Web documents, stream data mining, mining spatial and moving data, mining biological and medical data, and ranking.

Data mining tasks include prediction, classification, association rule mining, clustering, anomaly detection, ranking, etc.

Related fields are databases, machine learning, statistics, pattern recognition, bioinformatics, health informatics, etc.

IIS - Intelligent Information Systems Lab, Department of Computer Science and Engineering, Pohang University of Science and Technology

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