1st year     2nd year     3rd year     4th year    



B.Tech. IV (CO) Semester - 7 (ELECTIVE - I)

L

T

P

C

CO415 : DATA WAREHOUSING AND MINING (ELECTIVE - I)

3

1

0

4
COURSE OBJECTIVES
  • To introduce students to the basic concepts and techniques of Data Mining.
  • To introduce a wide range of clustering, estimation, prediction, and classification algorithms.
  • To introduce mathematical statistics foundations of the Data Mining Algorithms.
  • To introduce basic principles, concepts and applications of data warehousing.
COURSE OUTCOMES
After successful completion of this course, student will be able to
  • Identify the key processes of data mining, data warehousing and knowledge discovery process.
  • Understand the basic principles and algorithms used in practical data mining and their strengths and weaknesses.
  • Apply data mining techniques to solve problems in other disciplines in a mathematical way.
COURSE CONTENT
  • OVERVIEW

    • (03 Hours)

    Introduction, Data Mining Functionalities, Data Mining Issues, Data Mining Metrics, Data Mining from a Database Perspective.
  • DATA PREPROCESSING

    • (03 Hours)

    Introduction, Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, Data Reduction, Data Discretization.
  • CLASSIFICATION

    • (09 Hours)

    Statistical-Based Algorithms, Decision Tree -Based Algorithms, Neural Network -Based Algorithms, Rule-Based Algorithms, Other Classification Methods, Combining Techniques, Accuracy and Error Measures, Evaluating the Accuracy of a Classifier.
  • CLUSTERING

    • (08 Hours)

    Similarity and Distance Measures, Hierarchical Algorithms, Partitioned Algorithms, Clustering Large Databases, Clustering with Categorical Attributes.
  • ASSOCIATION RULES

    • (09 Hours)

    Basic Algorithms, Advanced Association Rule Techniques, Measuring the Quality of Rules.
  • INTRODUCTION TO DATA WAREHOUSING

    • (05 Hours)

    Definition, difference between database system and data warehouse, multidimensional data model, data cubes, process architecture.
  • APPLICATIONS AND OTHER DM TECHNIQUES

    • (05 Hours)

    Data Mining Applications, Mining Event Sequences, Visual DM, Text Mining, Web Mining, The WEKA data mining Workbench.
  • Tutorials will be based on the coverage of the above topics separately

    • (14 Hours)

    (Total Contact Time: 42 Hours + 14 Hours = 56 Hours)
    BOOKS RECOMMENDED

    1. J. Han and M. Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufman, 3/E, 2011.
    2. Ian H. Witten, Eibe Frank and Mark A. Hall, " Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufman, 3/E, 2011.
    3. Roiger, Richard, and Michael Geatz, "Data Mining: A tutorial-based primer", Addison Wesley, 2006.
    4. Tom Soukup, Davidson," Visual Data Mining: Techniques and Tools for Data Visualization and Mining", 1/E, Wiley, 2002.
    5. Alex Berson, Stephen J. Smith, "Data Warehousing, Data Mining, and OLAP", MGH, 1998.