Course

Discipline Specific Electives: Business Analytics

Unit 1

Data Mining: Datatypes of Data–Data Mining Functionalities– Interestingness Patterns– Classification of Data Mining systems– Data mining Task primitives –Integration of Data mining system with a data warehouse. There are major issues in data mining and data processing.

Unit 2

Association Rule Mining: Mining Frequent Patterns–Associations and Correlations – Mining Methods– Mining Various Association Rules– Correlation Analysis– Constraint-based Association mining. Graph Pattern Mining, SPM.

Unit 3

Classification: Classification and Prediction – Basic concepts–Decision tree induction– Bayesian classification, Rule–based classification, Lazy learner.

Unit 4

Clustering and Applications: Cluster analysis–Types of Data in Cluster Analysis– Categorization of Major Clustering Methods– Partitioning Methods, Hierarchical Methods– Density–Based Methods, Grid–Based Methods, Outlier Analysis.

Unit 5

Advanced Concepts: Basic concepts in Mining data streams–Mining Time–series data–– Mining sequence patterns in Transactional databases– Mining Object– Spatial– Multimedia– Text and Web data – Spatial Data mining– Multimedia Data mining–Text Mining– Mining the World Wide Web.

Objective:

To understand methods for mining frequent patterns, associations, and correlation and then describe data classification, prediction methods, and data–clustering approaches.

Course Outcome

CO1: Ability to understand the data types to be mined and present a general classification of tasks and primitives to integrate a data mining system.

CO2: Apply preprocessing methods for any given raw data.

CO3: Extract interesting patterns from large amounts of data and discover the role played by data mining in various fields.

CO4: Choose and employ suitable data mining algorithms to build analytical applications. CO5: Evaluate the accuracy of supervised and unsupervised models and algorithms.

References Textbook:

• Data Mining – Concepts and Techniques – Jiawei Han & Micheline Kamber, 3rd Edition
• Data Mining Introductory and Advanced topics – Margaret H Dunham, PEA