Download Data Mining and Warehousing

IT 302
Data Mining and Warehousing
3-1-0- 4
Total Lectures: 40
1. Introduction: Fundamentals of data mining, data mining functionalities, classification of data
mining systems, data mining task primitives, integration of a data mining system with a
database or a data warehouse system, major issues in data mining.
[4]
Need for Preprocessing, data cleaning, data integration and
2. Data Preprocessing:
transformation, data reduction, discretization and concept hierarchy generation.
[3]
3. Mining Frequent Patterns, Associations and Correlations: Basic concepts, efficient and
scalable frequent item set mining methods, mining various kinds of association rules, from
association mining to correlation analysis and constraint-based association mining.
[5]
4. Classification and Prediction: Classification and prediction, classification by decision tree
induction, Bayesian classification, rule-based classification, classification by back
propagation, support vector machines, associative classification, lazy learners, other
classification methods, prediction, accuracy and error measures, evaluating the accuracy of a
classifier or a predictor, ensemble methods.
[7]
Cluster
Analysis:
Types
of
data
in
cluster
analysis,
a
categorization
of
major
clustering
5.
methods, partitioning methods, hierarchical methods, density-based methods, grid based
methods, model-based clustering methods, clustering high-dimensional data, constraint based
cluster analysis and outlier analysis.
[7]
6. Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional analysis and
descriptive mining of complex data objects, spatial data mining, multimedia data mining, text
mining, data mining applications, data mining system products and research prototypes. [7]
7. Data Warehousing: Overview, definition, delivery process, difference between database
system and data warehouse, multi dimensional data model, data cubes, stars, snowflakes, fact
constellations, concept hierarchy, process architecture and three tier architecture.
[7]
Text Books:
1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan
Kaufmann Publishers, 3rd Edition, 2011.
2. Alex Berson and Stephen J. Smith, Data Warehousing, Data Mining & OLAP, Tata Mc Graw
Hill Edition, Tenth Reprint, 2007.
Reference Books:
1. Mehmed Kantardzic, Data mining concepts, models, and algorithm, Wiley Interscience, 2003.
2. Ian Witten, Eibe Frank, Data Mining, Practical Machine Learning Tools and Techniques,
third edition, Morgan Kaufmann, 2011.
.