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Hi Fellows, Check out the ebook of Data mining & warehousing for engineering students & BCA/MCA students. I will also share the lecture notes and other ebooks in PDF download format. This ebook covers the following modules. Basic Concepts of Data mining & warehousing Introduction, Meaning and characteristics of Data Warehousing, Online Transaction Processing (OLTP), Data Warehousing Models, Data warehouse architecture & Principles of Data Warehousing Data Mining.

Building a Data Warehouse Project. Structure of the Data warehouse, Data warehousing and Operational Systems, Organizing for building data warehousing, Important considerations – Tighter integration, Empowerment, Willingness Business Considerations: Return on Investment Design Considerations, Technical Consideration, Implementation Consideration, Benefits of Data warehousing. Managing and Implementing a Data Warehouse Project Project Management Process, Scope Statement, Work Breakdown Structure and Integration, Initiating a data warehousing project Project Estimation, Analyzing Probability and Risk, Managing Risk: Internal and External, Critical Path Analysis. Data Mining What is Data mining (DM)? Definition and description, Relationship and Patterns, KDD vs Data mining, DBMS vs Data mining, Elements and uses of Data Mining, Measuring Data Mining Effectiveness: Accuracy,Speed & Cost Data Information and Knowledge, Data Mining vs. Machine Learning, Data Mining Models. Issues and challenges in DM, DM Applications Areas.

Techniques of Data Mining Various Techniques of Data Mining Nearest Neighbour and Clustering.

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. • 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.

Password: alljntuworld.in Data Warehousing & Data Mining (DWDM) Materials & Notes. DWDM Unit Wise Lecture Notes and Study Materials in pdf format for.

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. 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. Joe Robinson Birdseed.