Data Mining and Data Warehousing syllabus

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Data Mining – Motivation and Applications, Database queries Vs DM queries Data: Data, Information and Knowledge
• The KDD process, Attribute Types: Nominal, Binary, Ordinal and Numeric attributes, Types of Data: Relational, Temporal, Time-series, Spatial, Text and Multimedia, Data streams, www
• Introduction to Data Preprocessing, Data Cleaning: Missing values, Noisy Data
• Data integration: Redundancy and Correlation Analysis only
• Data reduction: Attribute Subset Selection, Sampling
• Data Discretization: Binning, Histogram Analysis, Data Discretization by Data Transformation: Min-max normalization, z-score normalization and decimal scaling
• Dissimilarity of Numeric Data: Minskowski Distance and Euclidean distance Case Study: Download ZOO and Heart Disease Datasets from UCI Machine Learning Repository and study various types of attributes and format of training data.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Data Warehouse: Basic Concepts, What Is a Data Warehouse? Differences between Operational Database Systems and Data Warehouses, Data Warehousing: A Multitiered Architecture, Data Warehouse Models: Enterprise Warehouse, Data Mart, and Virtual Warehouse, Extraction, Transformation, and Loading, Metadata Repository, Data Warehouse Modeling: Data Cube and OLAP , Data Cube: A Multidimensional Data Model
• Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional Data Models
• Dimensions: The Role of Concept Hierarchies
• Measures: Their Categorization and Computation
• Typical OLAP Operations.
• Case Study: Design a retail sales data warehouse to analyze customer purchases, product performance, and regional sales trends.
• The warehouse should use a star schema with a central Sales Fact table linked to Customer, Product, Store, and Time dimensions, enabling queries on top-selling products, seasonal demand, and promotion impacts.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Market basket Analysis, Frequent item set, Closed item set, Association Rules, Apriori Algorithm, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, Mining Frequent Itemset without Candidate Generation: FP Growth Algorithm.
• Cluster Analysis: What Is Cluster Analysis? Requirements for Cluster Analysis
• Overview of Basic Clustering Methods, Partitioning Methods: K-Means: A Centroid-Based Technique k-Medoids: A Representative Object-Based Technique
• Distance Measures in Algorithmic Methods, Evaluation of Clustering, Assessing Clustering Tendency, Measuring Clustering Quality.
• Case Study: Apply the Apriori Algorithm to a retail sales dataset to discover frequent itemsets and generate association rules.
• The goal is to identify product combinations often purchased together, enabling insights for market basket analysis, cross-selling strategies, and promotional planning.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Classification: What Is Classification? Supervised and Unsupervised Learning, General Approach to Classification, Decision Tree Induction, Decision Tree Induction
• Bayes Classification Methods: Bayes’ Theorem, Naive Bayesian Classification
• Rule-Based Classification: Using IF-THEN Rules for Classification, Rule Extraction from a Decision Tree, Rule Induction Using a Sequential Covering Algorithm
• Lazy Learners-k-Nearest- Neighbor Classifiers
• Techniques to Improve Classification Accuracy: Introducing Ensemble Methods, Bagging, Boosting.
• Model Evaluation and Selection: Metrics for Evaluating Classifier Performance, Holdout Method and Random Subsampling, Cross-Validation, Bootstrap.
• Case Study: Download 2-3 Dataset from UCI data repository and try to run some of the classification algorithms on workbench like WEKA.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Introduction to Mining techniques*: Text Mining, Data stream mining, spatial and Temporal mining techniques, web mining and Recommender systems. (*All topics to be discussed at Conceptual Level).
• Mining Complex Data Types: Mining Sequence Data: Time-Series, and Biological Sequences, Mining Graphs and Networks.
• Case Study: Study Stock market prediction using historical price sequences to identify trends and forecast future values.