Disappointed with the Google search result of 'data warehousing books', I try to put. Data Warehousing in the Real World: A Step-by-step Guide for Building. Murray Sam Anahory; Parallel Processing Techniques for Data Warehousing. Primarily because it was written free from Inmon & Kimball influence, hence it. Data Warehousing In the Real World; Sam Anahory & Dennis Murray; 1997, Pearson. Data Mining; Pieter Adriaans & Dolf Zantinge; 1997, Pearson.

Latest Material Links DWDM – DWDM – DWDM – DWDM – DWDM – DWDM – DWDM – DWDM – DWDM – DWDM Old Material Links DWDM – DWDM – DWDM – DWDM – DWDM – Please find the more DWDM Notes ppt files download links below UNIT – I • Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining. 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. Efficient And Scalable Frequent Itemset Mining Methods Mining Various Kinds Of Association Rules, • From Associative Mining To Correlation Analysis, Constraint Based Association Mining.

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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.