Data Mining Course outline - University of Sargodha

Course Title: Data Mining

Course Code: CS-4442

Course Structure: Lectures: 3 / Labs: 0

Credit Hours: 3

Prerequisites: CMP-3450(Database Systems)

Course Objectives:

The course introduces students with basic applications, concepts, and techniques of data mining
and to develop their skills for using recent data mining software to solve practical problems in a
variety of disciplines.

Course Syllabus:

Data-Mining Concepts, Preparing the Data, Data Reduction, Learning From Data, Statistical
Methods, Decision Trees and Decision Rules, Artificial Neural Networks, Ensemble Learning,
Cluster Analysis, Association Rules, Web Mining and Text Mining, Visualization Methods, Data
Mining Tools: Weka, CBA and Yale, etc.

Course Outline:

1.     Data-Mining Concepts: Introduction, Data-Mining Process, Large Data Sets, Data
Warehouses for Data Mining, Business Aspects Data Mining. [TB1: Ch. 1]

2.      Preparing the Data: Raw Data- Representation, Characteristics, Transformation; Missing
Data, Time-Dependent Data, Outlier Analysis. [TB1: Ch. 2]

3.      Data Reduction: Dimensions of Large Data Sets, Feature Reduction, Relief Algorithm,
Entropy Measure for Ranking Features, PCA, Value Reduction, Feature Discretization:
ChiMerge Technique, Case Reduction. [TB1: Ch. 3]

4.      Learning From Data: Learning Machine, SLT, Types of Learning Methods, Common
Learning Tasks, SVMs, kNN: Nearest Neighbor Classifier, Model Selection versus
Generalization, Model Estimation. [TB1: Ch. 4]

5.      Statistical Methods: Statistical Inference, Assessing Differences in Data Sets, Bayesian
Inference, Predictive Regression, ANOVA, Logistic Regression, Log-Linear Models,
LDA. [TB1: Ch. 5]

6.      Decision Trees and Decision Rules: Decision Trees, Generating & Pruning Decision
Tree, CART Algorithm & Gini Index, Limitations of Decision Trees and Decision Rules.
TB1: Ch. 6]

7.      Artificial Neural Networks: Model of an Artificial Neuron, Architectures of ANNs,
Learning Process, Learning Tasks Using ANNs, Multilayer Perceptrons, Competitive
Networks and Competitive Learning, SOMs. [TB1: Ch.7]

8.      Ensemble Learning: Ensemble-Learning Methodologies, Combination Schemes for
Multiple Learners, Bagging and Boosting, AdaBoost. [TB: Ch. 8]

9.      Cluster Analysis: Clustering, Similarity Measures, Agglomerative Hierarchical
Clustering, Partitional Clustering, Incremental Clustering, DBSCAN Algorithm. BIRCH
Algorithm, Agglomerative Hierarchal and Partition Clustering Algorithms, Clustering
Validation. [TB: Ch. 9]

10.  Association Rules: Market-Basket Analysis, Algorithm Apriori, From Frequent Itemsets
to Association Rules, Improving the Efficiency of the Apriori Algorithm, FP Growth
Method, Associative-Classification Method, Multidimensional Association-Rules
Mining. [TB: Ch. 10]

11. Web Mining and Text Mining: Web Mining, Web Content, Structure, and Usage Mining,
HITS and LOGSOM Algorithms, Mining Path-Traversal Patterns, PageRank Algorithm,
Text Mining, Latent Semantic Analysis. [TB: Ch. 11]

12.  Visualization Methods: Perception and Visualization, Scientific Visualization and
Information Visualization, Parallel Coordinates, Radial Visualization, Visualization
Using Self-Organizing Maps, Visualization Systems for Data Mining

13. Data Mining Tools: Weka, CBA and Yale, etc.

Textbook(s):

•         Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic,
Wiley-IEEE Press; 2^nd Edition (August 16, 2011). ISBN-10: 0470890452

Reference Material:

•         Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in
Data Management Systems) by Jiawei Han, Micheline Kamber and Jian Pei, Morgan
Kaufmann; 3^rd Edition (2011). ISBN-10: 0123814790

•         Principles of Data Mining (Adaptive Computation and Machine Learning) by David J.
Hand, Heikki Mannila and Padhraic Smyth, A Bradford Book (August 1, 2001). ISBN-
10: 026208290X

•         Data Mining and Data Warehousing: Practical Machine Learning Tools Techniques by
Ram Kumar Singh and Amit Asthana, LAP LAMBERT Academic Publishing (2012).
ISBN-10: 3659118419

•         Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle
Basics (The Springer International Series in Engineering and Computer Science) by Bon
K. Sy and Arjun K., Springer; 1^st Edition (2003). ISBN-10: 1402076509

•         Building the Data Warehouse by William H. Inmon, Wiley; 4^th Edition (2005). ISBN-10:
0764599445

Note: This content is obtained from official documents of University of Sargodha and applied on BS Computer Science for Main Campus, Sub Campuses, and Affiliated Colleges.