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Free download of Sikkim Manipal University, SMU previous year, last year, sample question paper for  : [Data Warehousing & Data Mining] MC0088. Find question and answer for Part-A 2 marks, Part-B 4 Marks and Part-C 8 marks. Free download of question paper of previous year test.   Data Warehousing & Data Mining , MC0088 question paper includes multiple choice options and answer also.

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   SMU >> MCA >> MC0088

   Data Warehousing & Data Mining

This is the collection of Sikkim Manipal University (SMU) question paper for MCA - Data Warehousing & Data Mining. It will help you to prepare your examination. All questions are classified as per question type like PART - A of 2 marks, PART - B of 4 marks and PART - C of 8 marks same as actual examination. SMU question paper set which includes year 2018, 2017, 2016 SMU question papers of Data Warehousing & Data Mining are updated regularly and it is absolutely free to use. SMU Question paper includes multiple choice question and answer as same as real and original examination. It will help you to study and prepare for your final SMU examination.

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  1. BCA
  2. MCA
  3. BSc IT
  4. MSc IT
  5. MBA

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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.

  Get Questions        PART - A    PART - B    PART - C

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