Showing posts with label CS8075 Study Materials. Show all posts
Showing posts with label CS8075 Study Materials. Show all posts

CS8075 Data Warehousing and Data Mining Syllabus Notes Question Banks with answers

CS8075 Data Warehousing and Data Mining Syllabus Notes Question Paper Question Banks with answers Anna University

Anna University Data Warehousing and Data Mining Syllabus Notes Question Bank Question Papers Regulation 2017

Anna University CS8075 Data Warehousing and Data Mining Notes are provided below. CS8075 Notes all 5 units notes are uploaded here. here CS8075 Data Warehousing and Data Mining notes download link is provided and students can download the CS8075 MC Lecture Notes and can make use of it.
CS8075 Data Warehousing and Data Mining Syllabus Notes

CS8075 Data Warehousing and Data Mining Syllabus Regulation 2017

UNIT I DATA WAREHOUSING, BUSINESS ANALYSIS AND ON-LINE ANALYTICAL PROCESSING (OLAP)
Basic Concepts - Data Warehousing Components – Building a Data Warehouse – Database Architectures for Parallel Processing – Parallel DBMS Vendors - Multidimensional Data Model – Data Warehouse Schemas for Decision Support, Concept Hierarchies -Characteristics of OLAP Systems – Typical OLAP Operations, OLAP and OLTP.
UNIT II DATA MINING – INTRODUCTION
Introduction to Data Mining Systems – Knowledge Discovery Process – Data Mining Techniques – Issues – applications- Data Objects and attribute types, Statistical description of data, Data Preprocessing – Cleaning, Integration, Reduction, Transformation and discretization, Data Visualization, Data similarity and dissimilarity measures.
UNIT III DATA MINING - FREQUENT PATTERN ANALYSIS
Mining Frequent Patterns, Associations and Correlations – Mining Methods- Pattern Evaluation Method – Pattern Mining in Multilevel, Multi Dimensional Space – Constraint Based Frequent Pattern Mining, Classification using Frequent Patterns
UNIT IV CLASSIFICATION AND CLUSTERING
Decision Tree Induction - Bayesian Classification – Rule Based Classification – Classification by Back Propagation – Support Vector Machines –– Lazy Learners – Model Evaluation and Selection-Techniques to improve Classification Accuracy. Clustering Techniques – Cluster analysis-Partitioning Methods - Hierarchical Methods – Density Based Methods - Grid Based Methods – Evaluation of clustering – Clustering high dimensional data- Clustering with constraints, Outlier analysis-outlier detection methods.
UNIT V WEKA TOOL
Datasets – Introduction, Iris plants database, Breast cancer database, Auto imports database - Introduction to WEKA, The Explorer – Getting started, Exploring the explorer, Learning algorithms, Clustering algorithms, Association–rule learners.
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CS8075 Data Warehousing and Data Mining Notes Syllabus
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CS8075 Data Warehousing and Data Mining Syllabus
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CS8075 Data Warehousing and Data Mining Question Bank
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