Knowledge Discovery and Data Mining
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The Laboratory for Knowledge Discovery in Databases (KDD) is a research group in the Computing and Information Sciences (CIS) Department at Kansas State University. Its research emphasis is in the areas of applied artificial intelligence (AI) and knowledge-based software engineering (KBSE) for decision support systems.

More specifically, we are interested in machine learning, data mining and knowledge discovery from large spatial and temporal databases, human-computer intelligent interaction (HCII), and high-performance computation in learning and optimization. In our research, we look for ways to systematically decompose analytical learning problems based upon information theoretic and probabilistic criteria, so that the most appropriate machine learning methods may be applied to the resulting transformed problems.

One of the major challenges in this area is the design of unsupervised learning and bias (or hyperparameter) optimization methods to produce an effective decomposition of learning tasks. An interesting opportunity presented by this problem is that, by addressing the high-level control of inductive learning in a statistically sound fashion, we can improve our techniques for both model selection and model integration (as practiced in multimodal sensor fusion). We have developed and applied such approaches to multistrategy learning, which are potentially computation-intensive, to interesting analytical problems in the areas of decision support (uncertain reasoning) and control automation.

The goal of our work is to gain insight into the interaction between artifacts that adapt or learn - whether by Bayesian, neural, or genetic computation - and their users. Important examples of this interaction include data visualization in intelligent displays, software agents for distributed high-performance computation and information retrieval, and virtual environments for simulation and computer-assisted instruction.

This section is under construction. Check back soon. - [01 Jul 2001]

Databases and repositories that we are currently using:

This section is under construction. Check back soon. - [19 Feb 2002]

This section is under construction. Check back soon. - [01 Jul 2001]

• Data mining, ANNs, Bayesian networks, GAs (maintained by W. Hsu, Kansas State University and NCSA)
  This page emphasizes data mining applications and numerical learning systems such as artificial neural networks, Bayesian networks, and genetic and evolutionary computation.
  
• KDNuggets, the KDD Resource Page (maintained by G. Piatetsky-Shapiro, Xchange)
  This page is the most comprehensive knowledge discovery and data mining resource guide on the web. It links to the KDNuggets News newsletter, covers every aspect of KDD, and is features up-to-date information on meetings and publications.
• Machine learning resource page (maintained by D. Aha, Naval Research Lab)
  This page is the most comprehensive machine learning resource guide on the web. It covers applications and experimental data sets as well as professional organizations, tutorials, and publications.

This section is under construction. Check back soon. - [19 Feb 2002]

• Genetic wrappers for supervised inductive learning
• A genetic algorithm wrapper for variable selection (aka feature subset selection, attribute selection)
• A genetic algorithm wrapper for ensemble selection
• A genetic programming wrapper for constructive induction (specifically, feature construction)
• A search-based wrapper for variable partitioning
• Bayesian network inference for KDD applications
  • Large-scale belief updating and revision
  • Fusion, propagation and structure learning
  • Parallel exact Bayesian network inference for large-scale belief updating
  • Parallel approximate Bayesian network inference for real-time interactive decision support
    • Sampling: forward, backward, heuristic/self/adaptive importance sampling
    • Evolutionary Markov-chain Monte Carlo (MCMC) search: GA-based importance sampling
• Other soft computing methods for KDD
• Spatial statistics in precision agriculture and GIS/GPS applications

This section is under construction. Check back soon. - [01 Jul 2001]

• Journal Papers
• [HWRC01] DMKD 2001, to appear
• [HRW00] MLJ 2000
• Conference Papers
• [HG01] GECCO 2001, to appear
• [GH01] EuroGP 2001
• [HGGC00] GECCO 2000
• [HWRC00] GECCO 2000
• Technical Reports
• [Under construction]
• White Papers
• [Under construction]

This section is under construction. Check back soon. - [01 Jul 2001]

• Timetables / Milestones
• [Under Construction]
• To-Do Lists
• [Under Construction]
• Data Sets
• [Under Construction]
• Software Releases
• [Under Construction]
• Documentation
• [Under Construction]

• William H. Hsu
  Computing and Information Sciences
• David Ben-Arieh
  Industrial and Manufacturing Systems Engineering
• Shing I Chang
  Industrial and Manufacturing Systems Engineering

• Afrand Agah
• Cindy Forgie
• Haipeng Guo
• James A. Louis
• Mantena V. Raju
• Cecil P. Schmidt
• Aiming Wu
• Rajasekharareddy Yakkanti

• Matthew A. Durst
• Benjamin B. Perry
• James W. Plummer
• Julie A. Stilson

• Yuhong Cheng
• Steven M. Gustafson
• Laura M. Kruse