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Home / Syllabus / B.E Artificial Intelligence and Machine Learning / Third Year AI ML / Semester 5 / Data Warehousing and Mining

B.E Artificial Intelligence and Machine Learning Data Warehousing and Mining Syllabus - Mumbai University

This is the Third Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Data-Warehousing-and-Mining.pdf
Semester 5 · Third Year AI-ML · 3 credits · 100 marks

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Syllabus for Data Warehousing and Mining

Semester 5 · Third Year AI-ML · 3 credits · 100 marks

Module 1: Data Warehouse and OLAP

  • Data Warehousing, Dimensional Modeling and OLAP The Need for Data Warehousing; Data Warehouse Defined; Benefits of Data Warehousing ; Features of a Data Warehouse; Data Warehouse Architecture; Data Warehouse and Data Marts; Data Warehousing Design Strategies. Dimensional Model Vs ER Model; The Star Schema, The Snowflake Schema; Fact Tables and Dimension Tables; Factless Fact Table; Updates To Dimension Tables, Primary Keys, Surrogate Keys & Foreign Keys; Aggregate Tables; Fact Constellation Schema or Families of Star Need for Online Analytical Processing; OLTP vs OLAP; OLAP Operations in a cube: Roll-up, Drilldown, Slice, Dice, Pivot ; OLAP Models: MOLAP, ROLAP, HOLAP. Major steps in ETL Process

Module 2: Introduction to Data Mining ,Data Exploration and Data Preprocessing

  • Data Mining Task primitives,Architecture,KDD process,Issues in data Mining,Types of Attributes; Statistical Description of Data; Data Visualization; Measuring similarity and dissimilarity. Why Preprocessing? Data Cleaning; Data Integration; Data Reduction: Attribute subset selection, Histograms, Clustering and Sampling; Data Transformation & Data Discretization: Normalization, Binning, Histogram Analysis and Concept hierarchy generation.

Module 3: Classification

  • Basic Concepts; Classification methods: 1. Decision Tree Induction: Attribute Selection Measures, Tree pruning. 2. Bayesian Classification: Naïve Bayes‟ Classifier. Prediction: Structure of regression models; Simple linear regression, Multiple linear regression. Accuracy and Error measures, Precision, Recall

Module 4: Clustering

  • Cluster Analysis: Basic Concepts; Partitioning Methods: K-Means, KMediods; Hierarchical Methods: Agglomerative, Divisive, BIRCH; Density Based Methods: DBSCAN What are outliers? Types, Challenges; Outlier Detection Methods: Supervised, Semi Supervised, Unsupervised, Proximity based, Clustering Based

Module 5: Frequent Pattern

  • Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules; Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, A pattern growth approach for mining Frequent Itemsets; Mining Frequent itemsets using vertical data formats; Introduction to Mining Multilevel Association Rules and Multidimensional Association Rules; From Association Mining to Correlation Analysis, lift, ; Introduction to Constraint-Based Association Mining

Module 6: Web Mining

  • Introduction to Web content Mining, Crawlers, Personalization, Web structure mining, Page rank,, Clever, Web Usage Mining

Useful Links

  • 1 Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann 3nd Edition
  • 2 P. N. Tan, M. Steinbach, Vipin Kumar, ―Introduction to Data Mining‖, Pearson Education.
  • 3 Paulraj Ponniah, ―Data Warehousing: Fundamentals for IT Professionals‖, Wiley India.
  • 4 Raghu Ramakrishnan and Johannes Gehrke, ―Database Management Systems‖ 3rd Edition McGraw Hill
  • 5 Elmasri and Navathe, ―Fundamentals of Database Systems‖, 6th Edition, PEARSON Education
  • 1 Theraja Reema, ―Data Warehousing‖, Oxford University Press, 2009
  • 2 Ralph Kimball, Margy Ross, ―The Data Warehouse Toolkit: The Definitive Guide To Dimensional Modeling‖, 3rd Edition. Wiley India.
  • 3 Michael Berry and Gordon Linoff ―Mastering Data Mining- Art & science of CRM‖, Wiley Student Edition
  • 4 Michael Berry and Gordon Linoff ―Data Mining Techniques‖, 2nd Edition Wiley Publications
  • 1 https://www.coursera.org/learn/data-warehousing-business-intelligence
  • 2 https://www.coursera.org/specializations/data-mining-foundations-practice
  • 3 https://onlinecourses.nptel.ac.in/noc20_cs12/preview
  • 4 https://nptel.ac.in/courses/106105174

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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