Data Warehousing and Data Mining (BTCS 702-18)

Detailed Syllabus

UNIT 1: Data Warehousing and Data Mining Fundamentals

Data Warehousing Introduction:

• Design guidelines for data warehouse implementation
• Multidimensional Models
• OLAP:
  • Introduction
  • Characteristics
  • Architecture
  • Multidimensional view
• Efficient processing of OLAP Queries
• OLAP server Architecture: ROLAP versus MOLAP Versus HOLAP
• Data Cube:
  • Data cube operations
  • Data cube computation

Data Mining:

• What is data mining
• Challenges
• Data Mining Tasks
• Data:
  • Types of Data
  • Data Quality
  • Data Pre-processing
• Measures of Similarity and Dissimilarity

Duration: 10 hours

UNIT 2: Association Rules and Classification

Data Mining - Association Rules Mining:

• Introduction
• Naive algorithm
• Apriori algorithm
• Direct Hashing and Pruning (DHP)
• Dynamic Item set counting (DIC)
• Mining frequent pattern without candidate generation (FP-growth)
• Performance evaluation of algorithms

Classification:

• Introduction
• Decision tree
• Tree induction algorithms:
  • Split algorithm based on information theory
  • Split algorithm based on Gini index
• Naïve Bayes method
• Estimating predictive accuracy of classification method

Duration: 10 hours

UNIT 3: Cluster Analysis and Search Engines

Cluster Analysis:

• Introduction
• Partition methods
• Hierarchical methods
• Density based methods
• Dealing with large databases
• Cluster software

Search Engines:

• Characteristics of Search engines
• Search Engine Functionality
• Search Engine Architecture
• Ranking of web pages
• The search engine history
• Enterprise Search
• Enterprise Search Engine Software

Duration: 10 hours

UNIT 4: Web Data Mining

• Web Terminology and Characteristics
• Locality and Hierarchy in the web
• Web Content Mining
• Web Usage Mining
• Web Structure Mining
• Web mining Software

Duration: 8 hours

Suggested Readings / Books

1. Carlo Vercellis, Business Intelligence: Data mining and Optimization for Decision Making, WILEY
2. Han J., Kamber M. and Pei J., Data mining concepts and techniques, Morgan Kaufmann Publishers (2011) 3rd ed.
3. Pudi V., Krishana P.R., Data Mining, Oxford University press, (2009) 1st ed.
4. Adriaans P., Zantinge D., Data mining, Pearson education press (1996), 1st ed.
5. Pooniah P., Data Warehousing Fundamentals, Willey interscience Publication, (2001), 1st ed.