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Data Warehousing & Data Mining


This is the collection of Sikkim Manipal University (SMU) question and answers for Data Warehousing & Data Mining. It will help to prepare your examination. All question paper are classified as per semester, subject code and question type of Part A, Part B and Part C with multiple choice options as same as actual examination. SMU question papers includes year 2018, 2017, 2016 Sem I, II, III, IV, V, VI examinations of all subjects.

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Course Name        MCA (Master of Computer Application)

Subject Code       MC0088 (Data Warehousing & Data Mining)

Get Questions        PART - A    PART - B    PART - C

Data Warehousing & Data Mining Syllabus.

Part 1 - Introduction to Data Mining
Introduction; Meaning and Working of Data Mining; Data, Information and knowledge; Data Warehousing and Data Mining – Relation; Data Mining and knowledge Discovery; Data Mining and OLAP; Data Mining and Statistics; Data Mining Technologies; Data Mining Software.

Part 2 - Data Warehousing and OLAP
Introduction; Data Warehouse: Operational Database Systems versus Data Warehouses; A Multidimensional Data Model: Stars, Snowflakes, and Fact Constellations, Examples for Star, Snowflake, and Fact Constellation Schemas, Introducing Concept Hierarchies, OLAP Operations in the Multidimensional Data Model; Data Warehouse Architecture: The Design of a Data Warehouse: A Business Analysis Framework, A Three – Tier Data Warehouse Architecture, Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP.

Part 3 - Business Intelligence
Introduction to Business Intelligence; Business Intelligence Tools; Business Intelligence VS Data Warehouse; Business Intelligence VS Data Mining; Business Intelligence Infrastructure.

Part 4 - Data Preprocessing
Introduction; Data Cleaning: Missing Values, Noisy Data, Inconsistent Data; Data Integration and Transformation: Data Integration, Data Transformation; Data Reduction: Data Cube Aggregation, Dimensionality Reduction, Numerosity Reduction; Discretization and Concept Hierarchy Generation: Segmentation by Natural Partitioning.

Part 5 - Data Mining Techniques – An Overview
Introduction; Data Mining Vs DBMS; Data Mining Techniques: Association Rules, Classification, Regression, Clustering, Neural Networks.


Part 6 - Associations Rule Mining
Introduction; Market Basket Analysis; Association Rule; Association Rule Mining: A Road Map; Methods to Discover Association Rules; A Priori Algorithm; Partition Algorithm; Pincers – Search Algorithm; Dynamic Itemset Counting Algorithm; Fp – Tree Growth Algorithm; Mining Multilevel Association Rules from Transaction Databases: Multilevel Association Rules, Approaches to Mining Multilevel Association Rules, Checking for Redundant Multilevel Association Rules; Mining Multidimensional Association Rules from Relational Database and Data Warehouses: Multidimensional Association Rules, Mining Multidimensional Association Rules Using Static Discretization of Quantitative Attributes, Mining Quantitative Association Rules; From Association Analysis to Correlation Analysis; Constraint – Based Association Mining.

Part 7 - Clustering
Introduction; Clustering; Cluster Analysis; Clustering Methods: K-means, Hierarchical Clustering, Agglomerative Clustering, Divisive Clustering; Clustering and Segmentation Software; Evaluating Clusters.

Part 8 - Classification and Prediction
Introduction; Classification and Prediction; Issues Regarding Classification and Prediction: Preparing the Data for Classification and Prediction, Comparing Classification Methods; Classification by Decision Tree Induction: Decision Tree Induction, Tree Pruning, Extracting Classification Rules from Decision Trees.

Part 9 - Web Mining
Introduction; Terminologies; Categories of Web Mining: Web Content Mining, Web Structure Mining, Web Usage Mining; Applications of Web Mining; Agent Based and Data Base approaches; Web Mining Software.

Part 10 - Multimedia and Text Mining
Introduction; Multimedia Data Mining; Text Mining.

Part 11 - Applications of Data Mining
Introduction; Business Applications using Data Mining; Scientific Applications using Data Mining; New Applications.

Part 12 - Case Study:
Data Mining Techniques in Healthcare Industry.
 


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