IT6702 DATA WAREHOUSING AND DATA MINING THEORY | Impotent Questions | Question bank | Syllabus | Model and Previous Question papers | Download PDF

IT6702 DATA WAREHOUSING AND DATA MINING THEORY 

IT6702 DATA WAREHOUSING AND DATA MINING THEORY

IT6702 DATA WAREHOUSING AND DATA MINING THEORY  | Impotent  Questions | Question bank | Syllabus | Model and Previous Question papers | Download PDF Exammain.com Important Questions is available for download in this page for IT6702 DATA WAREHOUSING AND DATA MINING THEORY . Students can download the Important Questions in the PDF format or in Word format. Questions Bank and Previous year question papers also be available in this page  . Important Questions provided here are the Expected questions that are possible to be appeared in the upcoming exams. you can make use of the below questions and prepare for your exams. IT6702 DATA WAREHOUSING AND DATA MINING THEORY  Important Questions . Download the Important Questions using the below link.

Subject Informations : 

University  :  Anna University
Department  : Information Technology
Semester  : 07 th
Year  : 04 th year
Regulation  : R2013
Subject Code  : IT6702
Subject Name  : DATA WAREHOUSING AND DATA MINING THEORY


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IT6702 DATA WARE HOUSING AND DATA MINING THEORY SYLLABUS REGULATION 2013

UNIT I DATA WAREHOUSING

Data warehousing Components –Building a Data warehouse –- Mapping the Data Warehouse to a Multiprocessor Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools –Metadata.

UNIT II BUSINESS ANALYSIS

Reporting and Query tools and Applications – Tool Categories – The Need for Applications – Cognos Impromptu – Online Analytical Processing (OLAP) – Need – Multidimensional Data Model – OLAP Guidelines – Multidimensional versus Multirelational OLAP – Categories of Tools – OLAP Tools and the Internet.

UNIT III DATA MINING

Introduction – Data – Types of Data – Data Mining Functionalities – Interestingness of Patterns – Classification of Data Mining Systems – Data Mining Task Primitives – Integration of a Data Mining System with a Data Warehouse – Issues –Data Preprocessing.

UNIT IV ASSOCIATION RULE MINING AND CLASSIFICATION

Mining Frequent Patterns, Associations and Correlations – Mining Methods – Mining various Kinds of Association Rules – Correlation Analysis – Constraint Based Association Mining – Classification and Prediction – Basic Concepts – Decision Tree Induction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction.

UNIT V CLUSTERING AND TRENDS IN DATA MINING


Cluster Analysis – Types of Data – Categorization of Major Clustering Methods – K-means– Partitioning Methods – Hierarchical Methods – Density-Based Methods –Grid Based Methods – Model-Based Clustering Methods – Clustering High Dimensional Data – Constraint – Based Cluster Analysis – Outlier Analysis – Data Mining Applications.

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