Data Warehousing and Data Mining (CSC420) — Notes, Syllabus & Past Questions

Techniques for storing and discovering patterns in large data repositories.

Core Course9 Chapters
Chapter 1Past Questions

Introduction to Data Warehousing

Lifecycle of data, Types of data, Data warehouse and data warehousing, Differences between operational database and data warehouse, A multidimensional data model.

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Chapter 2Past Questions

Introduction to Data Mining

Motivation for data mining, Introduction to data mining system, Data mining functionalities, KDD, Data object and attribute types, Statistical description of data, Issues and Applications.

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Chapter 3Past Questions

Data Preprocessing

Data cleaning, Data integration and transformation, Data reduction, Data discretization and Concept Hierarchy Generation, Data mining primitives.

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Chapter 4Past Questions

Data Cube Technology

Efficient method for data cube computation, Cube materialization (Introduction to Full cube, Iceberg cube, Closed cube, Shell cube), General strategies for cube computation.

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Chapter 5Past Questions

Mining Frequent Patterns

Frequent patterns, Market basket analysis, Frequent itemsets, closed itemsets, association rules, Types of association rule (Single dimensional, multidimensional, multilevel, quantitative).

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Chapter 6Past Questions

Classification and Prediction

Definition (Classification, Prediction), Learning and testing of classification, Classification by decision tree induction, ID3 as attribute selection algorithm, Bayesian classification.

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Chapter 7Past Questions

Cluster Analysis

Types of data in cluster analysis, Similarity and dissimilarity between objects, Clustering techniques: Partitioning (k-means, k-means++, Mini-Batch k-means, k-medoids).

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Chapter 8Past Questions

Graph Mining and Social Network Analysis

Graph mining, Why graph mining, Graph mining algorithm (Beam search, Inductive logic programming), Social network analysis, Link mining, Friends of friends, Degree assortativity.

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Chapter 9Past Questions

Mining Spatial, Multimedia, Text and Web Data

Spatial data mining, Spatial data cube, Mining spatial association, Multimedia data mining, Similarity search in multimedia data, Mining association in multimedia data, An introduction to text mining.

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