DWDM Latest Material Links DWDM Old Material Links Please find the more DWDM ppt Notes files download links below UNIT – I • Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining. Karzzzz Movie Video Song Download. • Data Preprocessing: Needs Preprocessing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation. UNIT – II • Data Warehouse and OLAP Technology for Data Mining Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, • Data Warehouse Implementation, Further Development of Data Cube Technology, From Data Warehousing to Data Mining. Data cube computation and Data Generalization: • Efficient methods for Data cube computation, Further Development of Data Cube and OLAP Technology, Attribute Oriented Induction. UNIT – III • Mining Frequent Patterns, Associations And Correlations, Basic Concepts.

Analytical Geometry and Real and Complex. Software Engineering I. Computer Architecture. Programming in C. COBOL and Data Processing. C Programming Laboratory. Data Warehousing in the Real World: A practical guide for building Decision Support Systems [S. Murray] on Amazon.com. Database designs provided in the appendixAbout the Authors:Sam Anahory is Director for Systems Integration at SHL Systemhouse (UK) where he runs their Data Warehousing practice,. Physical Files. Bit-Mapped Index, Multilevel Indexes. Dynamic Multilevel Indexes Using B-Trees and B+-Trees. Data Warehouse and Data Mining. C) Adobe PDF file. • Please use ESET system on SCIS web site to submit your assignments. Please also keep your personal copies of all.

Efficient And Scalable Frequent Itemset Mining Methods Mining Various Kinds Of Association Rules, • From Associative Mining To Correlation Analysis, Constraint Based Association Mining. UNIT – IV • Classification and Prediction: Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, • Bayesian Classification, Classification by Backpropagation, Support Vector Machines, Associative Classification, Lazy Learners, • Other Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the accuracy of Classifier or a predictor, Ensemble methods. UNIT – V • Cluster Analysis Introduction: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, • Partitioning Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Outlier Analysis.

UNIT – VI • Mining Streams, Time Series and Sequence Data: Mining Data Streams Mining Time Series Data, Mining Sequence Patterns in Transactional Databases, • Mining Sequence Patterns in biological Data, Graph Mining, Social Network Analysis and Multi Relational Data Mining UNIT – VII • Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive mining of Complex Data objects, Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining of the World WideWeb. UNIT – VIII • Applications and Trends In Data Mining: Data mining applications, Data Mining Products and Research Prototypes, Additional Themes on Data Mining and Social Impacts Of Data Mining. TEXT BOOKS: • Data Mining – Concepts and Techniques – JIAWEI HAN & MICHELINE KAMBER Harcourt India.2nd ed 2006 • introduction to data mining- pang-ning tan, micheal steinbach and vipin kumar, pearson education. REFERENCES: • Data Mining Introductory and advanced topics –MARGARET H DUNHAM, PEARSON EDUCATION • Data Mining Techniques – ARUN K PUJARI, University Press. • Data Warehousing in the Real World – SAM ANAHORY & DENNIS MURRAY. Pearson Edn Asia.