JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Prerequisites:

• A course on “Database Management Systems”.

Course Objectives:

• Learn data mining concepts understand association rules mining.
• Discuss classification algorithms learn how data is grouped using clustering techniques.
• To develop the abilities of critical analysis to data mining systems and applications.
• To implement practical and theoretical understanding of the technologies for data mining.
• To understand the strengths and limitations of various data mining models.

Course Outcomes:

• Ability to perform the preprocessing of data and apply mining techniques on it.
• Ability to identify the association rules, classification and clusters in large data sets.
• Ability to solve real world problems in business and scientific information using data mining.
• Ability to classify web pages, extracting knowledge from the web.

UNIT - I

Introduction to Data Mining: Introduction, What is Data Mining, Definition, KDD, Challenges, Data Mining Tasks, Data Pre-processing, Data Cleaning, Missing data, Dimensionality Reduction, Feature Subset Selection, Discretization and Binarization, Data Transformation; Measures of Similarity and Dissimilarity- Basics.

UNIT - II

Association Rules: Problem Definition, Frequent Item Set Generation, The APRIORI Principle, Support and Confidence Measures, Association Rule Generation; APRIOIRI Algorithm, The Partition Algorithms, FP-Growth Algorithms, Compact Representation of Frequent Item Set- Maximal Frequent Item Set, Closed Frequent Item Set.

UNIT - III

Classification: Problem Definition, General Approaches to solving a classification problem, Evaluation of Classifiers, Classification techniques, Decision Trees-Decision tree Construction, Methods for Expressing attribute test conditions, Measures for Selecting the Best Split, Algorithm for Decision tree Induction; Naive-Bayes Classifier, Bayesian Belief Networks; K- Nearest neighbor classification-Algorithm and Characteristics.

UNIT - IV

Clustering: Problem Definition, Clustering Overview, Evaluation of Clustering Algorithms, Partitioning Clustering- K-Means Algorithm, K-Means Additional issues, PAM Algorithm; Hierarchical Clustering-Agglomerative Methods and divisive methods, Basic Agglomerative Hierarchical Clustering Algorithm, Specific techniques, Key Issues in Hierarchical Clustering, Strengths and Weakness; Outlier Detection.

UNIT - V

Web and Text Mining: Introduction, web mining, web content mining, web structure mining, we usage mining, Text mining –unstructured text, episode rule discovery for texts, hierarchy of categories, text clustering.

TEXTBOOKS:

• 1. Data Mining- Concepts and Techniques- Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers, Elsevier, Edition, 2006.
• 2. Introduction to Data Mining, Pang-Ning Tan, Vipin Kumar, Michael Steinbanch, Pearson Education.
• 3. Data mining Techniques and Applications, Hongbo Du Cengage India Publishing.

REFERENCES:

• 1. Data Mining Techniques, Arun K Pujari, 3rd Edition, Universities Press.
• 2. Data Mining Principles & Applications – T.V Sveresh Kumar, B.Esware Reddy, Jagadish S Kalimani, Elsevier.
• 3. Data Mining, Vikaram Pudi, P Radha Krishna, Oxford University Press.