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Syllabus: Tpcs in Data Base SysApplcn - CS-580-C01 Topic - Data Mining Summer07

 
Section Information: CS-580-C01 Topic - Data Mining Summer07
 
   
Course Name   Tpcs in Data Base SysApplcn

 
   
Course Description   Tpcs in Data Base Sys/Applcn

Section Instructor: Dr. Zdravko Markov
 
   
E-mail   markovz@ccsu.edu

Biography
 
   
  Dr. Zdravko Markov has an M.S. in Mathematics and Computer Science and a Ph.D. in Artificial Intelligence. He has been teaching and doing research in the area of Machine Learning for more than 15 years. Recently he developed a novel approach to conceptual clustering and is studying its application to Data Mining tasks. Dr. Markov has published 4 textbooks and more than 50 research papers in conference proceedings and journals. His most recent book (co-authored with Daniel Larose) is “Data Mining The Web: Uncovering Patterns in Web Content, Structure, and Usage", published by Wiley in 2007. Dr. Markov’s CCSU courses are in the areas of Computer Architecture and Design, Computing and Communication technology, Machine Learning, Data and Web Mining.

Course Dates
 
   
  May 29, 2007 - July 19, 2007

Course Description
 
   
  Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples. Special emphasis will be give to the Machine Learning methods as they provide the real knowledge discovery tools. Important related technologies, as data warehousing and on-line analytical processing (OLAP) will be also discussed. The students will use recent Data Mining software. Enrollment in this course is limited to 20 students.

Course Goals
 
   
 
  • To introduce students to the basic concepts and techniques of Data Mining.
  • To develop skills of using recent data mining software for solving practical problems.
  • To gain experience of doing independent study and research.

    • Required Textbook
     
       
      Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005, ISBN: 0-12-088407-0.

    Required Software
     
       
      Data Mining System with Free Open Source Machine Learning Software in Java. Available at http://www.cs.waikato.ac.nz/~ml/weka/index.html

    Grading
     
       
     

    Grading will be based on six assignments (60%), two quizzes (20%) and class participation through four scheduled discussions (20%). Assignments and qizzes will be graded on a 100 point scale, discussions - on a 50 point scale. Thus the maximum course total will be 1000 points.

    The letter grade will be determined by the following grading scale:
    A A- B+ B B- C+ C C- D+ D D- F
    950-1000 900-949 870-899 840-869 800-839 770-799 740-769 700-739 670-699 640-669 600-639 0-599

    Late assignments will be marked one letter grade down for each 3 days they are late.

    It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty or a final grade of F.


    Course Content (12 units)
     
       
     
    1. Introduction to Data Mining
      • What is data mining?
      • Related technologies - Machine Learning, DBMS, OLAP, Statistics
      • Data Mining Goals
      • Stages of the  Data Mining Process
      • Data Mining Techniques
      • Knowledge Representation Methods
      • Applications
      • Example: weather data
    2. Data Warehouse and OLAP
      • Data Warehouse and DBMS
      • Multidimensional data model
      • OLAP operations
      • Example: loan data set
    3. Data preprocessing
      • Data cleaning
      • Data transformation
      • Data reduction
      • Discretization and generating concept hierarchies
      • Installing Weka 3 Data Mining System
      • Experiments with Weka - filters, discretization
    4. Data mining knowledge representation
      • Task relevant data
      • Background knowledge
      • Interestingness measures
      • Representing input data and output knowledge
      • Visualization techniques
      • Experiments with Weka - visualization
    5. Attribute-oriented analysis
      • Attribute generalization
      • Attribute relevance
      • Class comparison
      • Statistical measures
      • Experiments with Weka - using filters and statistics
    6. Data mining algorithms: Association rules
      • Motivation and terminology
      • Example: mining weather data
      • Basic idea: item sets
      • Generating item sets and rules efficiently
      • Correlation analysis
      • Experiments with Weka - mining association rules
    7. Data mining algorithms: Classification
      • Basic learning/mining tasks
      • Inferring rudimentary rules: 1R algorithm
      • Decision trees
      • Covering rules
      • Experiments with Weka - decision trees, rules
    8. Data mining algorithms: Prediction
      • The prediction task
      • Statistical (Bayesian) classification
      • Bayesian networks
      • Instance-based methods (nearest neighbor)
      • Linear models
      • Experiments with Weka - Prediction
    9. Evaluating what's been learned
      • Basic issues
      • Training and testing
      • Estimating classifier accuracy (holdout, cross-validation, leave-one-out)
      • Combining multiple models (bagging, boosting, stacking)
      • Minimum Description Length Principle (MLD)
      • Experiments with Weka - training and testing
    10. Mining real data
      • Preprocessing data from a real medical domain (310 patients with Hepatitis C).
      • Applying various data mining techniques to create a comprehensive and accurate model of the data.
    11. Clustering
      • Basic issues in clustering
      • First conceptual clustering system: Cluster/2
      • Partitioning methods: k-means, expectation maximization (EM)
      • Hierarchical methods: distance-based agglomerative and divisible clustering
      • Conceptual clustering: Cobweb
      • Experiments with Weka - k-means, EM, Cobweb
    12. Advanced techniques, Data Mining software and applications
      • Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
      • Bayesian approach to classifying text
      • Web mining: classifying web pages, extracting knowledge from the web
      • Data Mining software and applications

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