BCA-III (CBCS) Sem: V Paper Type: DSC 1F Paper No: Paper XV Paper Name: Data Warehousing and Data Mining Credit: 04 Theory: 4 Hrs./week Marks: UA: 80 CA: 20 Total: 100 Course Objectives: 1. To understand the principles of Data warehousing and Data Mining. 2. To understand the Architecture of a Data Mining system. 3. To perform classification, association, and prediction of data. Course Outcomes: The students should be able to: 1. Identify data mining problems and implement the data warehouse. 2. Write association rules for a given data pattern. 3. Choose classification and clustering solutions. Unit 1: Introduction to Data Warehouse and Data Mining [15] Differences between Operational Database Systems and Data Warehouses, Data Warehouse Architecture, Data Warehouse Components, A Multidimensional Data Model, Schemas, Data Warehouse Implementation, Data cube Technology, OLAP operations, Data mining query language, Data Mining:- What is data mining, Evolution, KDD, What kind of data, Architecture, data mining views, Data Mining Functionalities, Issues in Data Mining. Unit 2: Data Preprocessing and Association Rule mining [15] Data Preprocessing:An Overview, Extract, Transform, Load (ETL) Processes, Data Cleaning, Data Integration, Data Transformation and Data Discretization, Data Reduction, Frequent Patterns, Associations, and Correlations: Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules, Frequent Itemset Mining Methods-Apriori Algorithm: Finding Frequent Itemsets, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, A Pattern-Growth Approach for Mining Frequent Itemsets, Mining Multilevel and multidimensional Association Rules, Constraint-Based Frequent Pattern Mining.Unit 3: Supervised Learning Technique [15] supervised and unsupervised learning, What Is Classification? What is regression, difference between classification and regressing, General Approach to Classification, Issues regarding Classification and Predication, Binary and Multiclass Classification, Types of classifications, Classification by Decision tree induction, Bayesian Classification, Classification by Back propagation, Logistic regression, k-Nearest-Neighbor Classifiers, SVM, Introducing Ensemble Methods-Bagging, Boosting, AdaBoost, Random Forests, Other classification methods, Prediction: regression. Model Evaluation and Selection-Metrics for Evaluating Classifier Performance, Cross-Validation, underfitting and overfittin. Unit 4: Unsupervised Learning Technique and Applications [15] Clustering: What is Cluster Analysis, Types of data in Cluster Analysis, A Categorization of Major Clustering Methods., Partitioning Methods, Hierarchical Methods, Density-Based Methods, Model-Based Clustering Methods: Statistical Approach, Neural Network Approach, Outlier Analysis, Applications and Trends in Data Mining: Data Mining Applications, Data Mining for Financial Data Analysis, Data Mining for Re
BCA-III (CBCS) Sem: V Paper Type: DSC 1F Paper No: Paper XV Paper Name: Data Warehousing and Data Mining Credit: 04 Theory: 4 Hrs./week Marks: UA: 80 CA: 20 Total: 100 Course Objectives: 1. To understand the principles of Data warehousing and Data Mining. 2. To understand the Architecture of a Data Mining system. 3. To perform classification, association, and prediction of data. Course Outcomes: The students should be able to: 1. Identify data mining problems and implement the data warehouse. 2. Write association rules for a given data pattern. 3. Choose classification and clustering solutions. Unit 1: Introduction to Data Warehouse and Data Mining [15] Differences between Operational Database Systems and Data Warehouses, Data Warehouse Architecture, Data Warehouse Components, A Multidimensional Data Model, Schemas, Data Warehouse Implementation, Data cube Technology, OLAP operations, Data mining query language, Data Mining:- What is data mining, Evolution, KDD, What kind of data, Architecture, data mining views, Data Mining Functionalities, Issues in Data Mining. Unit 2: Data Preprocessing and Association Rule mining [15] Data Preprocessing:An Overview, Extract, Transform, Load (ETL) Processes, Data Cleaning, Data Integration, Data Transformation and Data Discretization, Data Reduction, Frequent Patterns, Associations, and Correlations: Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules, Frequent Itemset Mining Methods-Apriori Algorithm: Finding Frequent Itemsets, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, A Pattern-Growth Approach for Mining Frequent Itemsets, Mining Multilevel and multidimensional Association Rules, Constraint-Based Frequent Pattern Mining.Unit 3: Supervised Learning Technique [15] supervised and unsupervised learning, What Is Classification? What is regression, difference between classification and regressing, General Approach to Classification, Issues regarding Classification and Predication, Binary and Multiclass Classification, Types of classifications, Classification by Decision tree induction, Bayesian Classification, Classification by Back propagation, Logistic regression, k-Nearest-Neighbor Classifiers, SVM, Introducing Ensemble Methods-Bagging, Boosting, AdaBoost, Random Forests, Other classification methods, Prediction: regression. Model Evaluation and Selection-Metrics for Evaluating Classifier Performance, Cross-Validation, underfitting and overfittin. Unit 4: Unsupervised Learning Technique and Applications [15] Clustering: What is Cluster Analysis, Types of data in Cluster Analysis, A Categorization of Major Clustering Methods., Partitioning Methods, Hierarchical Methods, Density-Based Methods, Model-Based Clustering Methods: Statistical Approach, Neural Network Approach, Outlier Analysis, Applications and Trends in Data Mining: Data Mining Applications, Data Mining for Financial Data Analysis, Data Mining for Re
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This guide outlines the essential steps for building a robust data warehouse and leveraging data mining techniques. It begins with establishing a solid foundation by comparing OLTP and warehouse systems, followed by data preparation and association mining using methods like Apriori. The application of supervised learning techniques is explored, including model evaluation, while unsupervised learning focuses on clustering and outlier detection. Finally, it integrates outcomes with industry...