Kansas State University Department of Computing and Information Sciences Kansas State University KDD Lab ( www.kddresearch.org ) Collaborative Filtering Collaborative Filtering Intelligent Information Retrieval and Intelligent Information Retrieval and the Grid the Grid Friday 11 October 2002 William H. Hsu Laboratory for Knowledge Discovery in Databases Department of Computing and Information Sciences Kansas State University http://www.kddresearch.org This presentation is: http://www.kddresearch.org/KSU/CIS/KU-20021010.ppt
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Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
Collaborative FilteringCollaborative FilteringIntelligent Information Retrieval and the GridIntelligent Information Retrieval and the Grid
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
AcknowledgementsAcknowledgements
• Kansas State University Lab for Knowledge Discovery in Databases– Graduate research assistants: Haipeng Guo ([email protected]), Roby
Joehanes ([email protected])– Other grad students: Prashanth Boddhireddy, Siddharth Chandak, Ben
B. Perry, Rengakrishnan Subramanian– Undergraduate programmers: James W. Plummer, Julie A. Thornton
• Joint Work with– KSU Bioinformatics and Medical Informatics (BMI) group: Sanjoy Das
(EECE), Judith L. Roe (Biology), Stephen M. Welch (Agronomy)– KSU Microarray group: Scot Hulbert (Plant Pathology), J. Clare Nelson
(Plant Pathology), Jan Leach (Plant Pathology)– Kansas Geological Survey, Kansas Biological Survey, KU EECS
• Other Research Partners– NCSA Automated Learning Group (Michael Welge, Tom Redman)– University of Manchester (Carole Goble, Robert Stevens)– The Institute for Genomic Research (John Quackenbush, Alex Saeed)– International Rice Research Institute (Richard Bruskiewich)
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
OverviewOverview
• Filtering– Collaborative filtering (CF) and relatives
– Application to intelligent information retrieval (IR)
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
Computational Genomics andComputational Genomics andMicroarray Data MiningMicroarray Data Mining
Treatment 1(Control)
Treatment 2(Pathogen)
Messenger RNA(mRNA) Extract 1
Messenger RNA(mRNA) Extract 2
cDNA
cDNA
DNA Hybridization Microarray(under LASER)
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
Publication(e.g., PubMed)
Source(e.g.,
Taxonomy)
Gene(e.g., GenBank)
Experiment
Sample Hybridization Array
Normalization/Discretization
Data
Components of A Microarray Experiment:Components of A Microarray Experiment:HybridizationHybridization
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
ComputationalWorkflows
(e.g., myGrid)
ExperimentalServices &Metadata
(Mage-ML XML)
GeneExpression
Model
Pathway &NetworkLearning
Specification
DataPreprocessingSpecification
ParameterLearning
Specification
ModelAnalysis
Specification
DiscretizationUse Case
Data MiningUse Case
Feature Selection
Specification
Validation(e.g., Bootstrap)
Use Case
Components of A Microarray Experiment:Components of A Microarray Experiment:Computational Gene Expression ModelingComputational Gene Expression Modeling
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
Graphical Models of Probability for Graphical Models of Probability for CCollaborative ollaborative FFiltering (CF)iltering (CF)
• Goal: Estimate
• Filtering: r = t
– Intuition: infer current state from observations
– Applications: signal identification
– Variation: Viterbi algorithm
• Prediction: r < t
– Intuition: infer future state
– Applications: prognostics
• Smoothing: r > t
– Intuition: infer past hidden state
– Applications: signal enhancement
• CF Tasks
– Plan recognition by smoothing
– Prediction cf. WebCANVAS – Cadez et al. (2000)
)y|P(X r1it
Murphy (2002)
Kansas State UniversityDepartment of Computing and Information Sciences
Kansas State University KDD Lab (www.kddresearch.org)
Tools for Building Graphical ModelsTools for Building Graphical Models