Data Warehousing and Data Mining SOLVED PAPERS AND GUESS
Product Details: Rohtak UNIVERSITY Data Warehousing and Data Mining
Format: BOOK
Pub. Date: NEW EDITION APPLICABLE FOR Current EXAM
Publisher: MEHTA SOLUTIONS
Edition Description: 2021-22
RATING OF BOOK: EXCELLENT
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If you find yourself getting fed up and frustrated with other Rohtak UNIVERSITY book solutions now mehta solutions brings top solutions for Rohtak UNIVERSITY Data Warehousing and Data Mining REPORT book contains previous year solved papers plus faculty important questions and answers specially for Rohtak UNIVERSITY .questions and answers are specially design specially for Rohtak UNIVERSITY students .
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Data Warehousing and Data Mining
UNIT-I
Introduction: The Evolution of Data Warehousing the Data Warehouse A Brief History, Today’s Development Environment; Principles of Data; Warehousing (Architecture and Design Techniques): Types of Data and their uses conceptual Data, Architecture, Design Techniques, Introduction to the Logical Architecture; Creating the Data Asset: Business Data Warehouse Design, Populating the Data Warehouse, Unlocking the Data Asset for End Users (The Use of Business Information).
UNIT-II
Designing Business Information Warehouse; Populating Business Information Warehouse, User Access to Information, Information, Data in Context. Data Mining Introduction: Motivation, Importance, data mining, kind of data, Functionalities, Interesting Patterns, Classification of data mining systems, Major issues; Data Warehouse and OLAP Technology for Data Mining: Data warehouse, operational database systems and data warehouses, Architecture, Implementation, development of data cube technology, data warehousing to data mining, Data warehouse usage.
UNIT-III
Data Preparation: Preprocess, Data cleaning, Data integration and transformation, Data reduction, Discrete and concept hierarchy generation; Data Mining Primitives: Languages, and System Architecture, graphical user interfaces; Concept Description: Characterization and Comparison, Data generalization and summarization based characterization, Analytical characterization: analysis of attribute relevance, mining class comparisons, Mining descriptive statistical measures in large database.
UNIT-IV
Mining Association Rules in Large Database: Mining single dimensional Boolean association rules from transaction database, Mining multidimensional association rules from database and data warehouses, from associating mining to correlation analysis, Constraint based association mining; Classification and Prediction:Issues, classification by decision tree induction, Bayesian classification, Classification by back propagation; Classification based on concepts from association rule mining; Other classification methods. Lab: Each student is required to develop at least one data-house.
