2011 Fall Term

Unsupervised Leaning

• Machine Learning/Inductive Learning
  • Slides Introduction to machine learning 
  • Slides Introduction to inductive learning 
  • Introduction to machine learning, Nils J. Nilsson. Chapter 1 
• Data Mining, a global perspective
  • Slides Introduction to Data Mining
  • From Data Mining To knowledge Discovery in Databases, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth
• Data preprocessing/transformation
  • Slides Data preprocessing/Transformation
    
  • Algorithms for Clustering Data, Chapter 2 , Jain & Dubes
  • Continuous attributes discretization
  • Dimensionality Reduction: PCA, ICA, Multidimensional Scalling, Random Projection, Locally linear embedding, ISOMAP
  • Dimensionality reduction: Checkout also the sections 5 to 9 of chapter 14 of the book "The Elements of Statistical Learning" Hastie, Tibshirani, Friedman
    
• Numerical taxonomy, an statitstical approach. Unsupervised machine learning, an artificial intelligence approach
  • Slides about numerical taxonomy and unsupervised learning
  • Slides about clustering evaluation
  • Numerical taxomomy
  • Data Clustering: A Review (1999) A K Jain, M N Murty, P J Flynn ACM Computing Surveys
  • Algorithms for Clustering Data, Capitulo 3 , Jain & Dubes
  • Checout also sections 1 to 8 of of the book "The Elements of Statistical Learning" Hastie, Tibshirani, Friedman
  • Unsupervised learning
  • Concepts and Conceptual Structure D. L. Medin
  • Learning from observation: Conceptual Clustering R. Michalski, R. Stepp
  • Models of incremental concept formation J. Gennari, P. Langley, D. Fisher
  • P. Cheeseman, J. Stutz, "Bayesian Classification (AutoClass): Theory and Results", in Advances in Knowledge Discovery and Data Mining, Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, & Ramasamy Uthurusamy, Eds. AAAI Press/MIT Press, 1996.
  • Evaluaton of clustering
  • Unsupervised learning evaluation (part 1 y part 2)
  • Comparison of clusterings
• Semi supervised Clustering
  
• Introduction to semi supervised Clustering
  • S. Basu, A. Banerjee, R. Mooney,"semi-supervised clustering by seeding"  , ICML-2002
    
  • S. Basu, M. Bilenko, R. Mooney,"A probabilistic framework for semi-supervised clustering", 10th ACM SIGKDD 2004
  • D. Cohn, R. Caruana, A. McCallum, "Semi-supervised Clustering with user feedback", TR2003-1892, Cornell University, 2003
    
•  Domain Theories
• J. Bejar, Improving Knowledge Discovery using Domain Knowledge in Unsupervised Learning , ECML 2000
• Unsupervised methodologies in Knowledge Discovery and Data Mining
• Slides Clustering methodologies in Knowledge discovery
• Slides Association Rules (examples)
• Slides Mining sequential and structured data
• Unsupervised methodologies in Data Mining
  • Survey of clustering data mining techniques Pavel Berkhin
  • Scaling Clustering Algorithms to Large Databases (1998) P.S. Bradley, Usama Fayyad, Cory Reina Knowledge Discovery and Data Mining
  • CLARANS: A Method for Clustering Objects for Spatial Data Mining Raymond T. Ng, Jiawei Han
  • BIRCH: An Efficient Data Clustering Method for Very Large Databases (1996) Tian Zhang, Raghu Ramakrishnan, Miron Livny
  • CURE: An efficient clustering algorithm for large databases Sudipto Guha , Rajeev Rastogi , and Kyuseok Shim
  • ROCK: A ROBUST CLUSTERING ALGORITHM FOR CATEGORICAL ATTRIBUTES Sudipto Guha , Rajeev Rastogi , and Kyuseok Shim
  • CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling George Karypis, Eui-Hong (Sam) Han, Vipin Kumar
  • A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (DBSCAN) Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu
  • OPTICS: Ordering Points To Identify the Clustering Structure Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, J&g Sander
  • STING: A statistical information GRID approach to spatial datamining Wang, Yang, Muntz
  • An efficient approach to clustering in large multimedia databases witn noise (DENCLUE) Hinnenburg, Keim
    
  • Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications (CLIQUE) (1998) Rakesh Agrawal Johannes Gehrke Dimitrios Gunopulos Prabhakar Raghavan
  • MAFIA: Efficient and scalable subspace clustering for very large datasets S. Goil, H. Nagesh, A. Choudhary
• Association Rules
  
  • Interest indices for association rules
  • Checkout the sample chapter about association rules from the book Introduction to Data Mining Tan Steinbach, Kumar
  • Association Rule Mining: A Survey Qiankun Zhao Sourav S. Bhowmick
  • J. Han, J. Pei, Y. Yin and R. Mao, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, 8(1):53-87, 2004.
• Mining sequential and structure data
  • Discovery of frequent episodes in event sequences Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. Data Mining and Knowledge Discovery 1(3): 259 - 289, November 1997.
  • Jaak Vilo Discovering Frequent Patterns from Strings. Technical Report C-1998-9 (pp. 20) May 1998. Department of Computer Science, University of Helsinki.
  • Mining Sequential Patterns: Generalizations and Performance Improvements Ramakrishnan Srikant, Rakesh Agrawal (1996)
  • Mining Sequential Patterns Rakesh Agrawal, Ramakrishnan Srikant (1995)
  • An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data Akihiro Inokuchi, Takashi Washio and Hiroshi Motoda
  • gSpan: Graph-Based Substructure Pattern Mining. Xifeng Yan, Jiawei Han. UIUC Technical Report, UIUCDCS-R-2002-2296, 2002.
  • Frequent Free Tree Discovery in Graph Data Ulrich Rückert, Stefan Kramer In: Special Track on Data Mining, ACM Symposium on Applied Computing (SAC2004), 2004.
• Bayesian networks
  • Slides Learning of bayesian networks (Examples)
  • Learning Probabilistic Networks Paul J Krause
    
• Additional themes