Description This unique free application is for all students of Data Mining & Data Warehousing across the world. It covers 200 topics of Data Mining & Data Warehousing in detail. These 200 topics are divided in 5 units. Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail. The USP of this application is 'ultra-portability'. Students can access the content on-the-go from any where they like. Basically, each topic is like a detailed flash card and will make the lives of students simpler and easier. Some of topics Covered in this application are: 1. Introduction to Data mining 2. Data Architecture 3. Data-Warehouses 4. Relational Databases 5. Transactional Databases 6. Advanced Data and Information Systems and Advanced Applications 7. Data Mining Functionalities 8. Classification of Data Mining Systems 9. Data Mining Task Primitives 10. Integration of a Data Mining System with a DataWarehouse System 11. Major Issues in Data Mining 12. Performance issues in Data Mining 13. Introduction to Data Preprocess 14. Descriptive Data Summarization 15. Measuring the Dispersion of Data 16. Graphic Displays of Basic Descriptive Data Summaries 17. Data Cleaning 18. Noisy Data 19. Data Cleaning Process 20. Data Integration and Transformation 21. Data Transformation 22. Data Reduction 23. Dimensionality Reduction 24. Numerosity Reduction 25. Clustering and Sampling 26. Data Discretization and Concept Hierarchy Generation 27. Concept Hierarchy Generation for Categorical Data 28. Introduction to Data warehouses 29. Differences between Operational Database Systems and Data Warehouses 30. A Multidimensional Data Model 31. A Multidimensional Data Model 32. Data Warehouse Architecture 33. The Process of Data Warehouse Design 34. A Three-Tier Data Warehouse Architecture 35. Data Warehouse Back-End Tools and Utilities 36. Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP 37. Data Warehouse Implementation 38. Data Warehousing to Data Mining 39. On-Line Analytical Processing to On-Line Analytical Mining 40. Methods for Data Cube Computation 41. Multiway Array Aggregation for Full Cube Computation 42. Star-Cubing: Computing Iceberg Cubes Using a Dynamic Star-tree Structure 43. Pre-computing Shell Fragments for Fast High-Dimensional OLAP 44. Driven Exploration of Data Cubes 45. Complex Aggregation at Multiple Granularity: Multi feature Cubes 46. Attribute-Oriented Induction 47. Attribute-Oriented Induction for Data Characterization 48. Efficient Implementation of Attribute-Oriented Induction 49. Mining Class Comparisons: Discriminating between Different Classes 50. Frequent patterns 51. The Apriori Algorithm 52. Efficient and scalable frequently itemset mining methods 53. Mining Frequent Itemsets Using Vertical Data Format 54. Mining Multilevel Association Rules 55. Mining Multidimensional Association Rules 56. Mining Quantitative Association Rules 57. Association Mining to Correlation Analysis 58. Constraint-Based Association Mining 59. Introduction to classification and prediction 60. Preparing the Data for Classification and Prediction 61. Comparing Classification and Prediction Methods 62. Classification by Decision Tree Induction 63. Decision Tree Induction 64. Tree Pruning 65. Scalability and Decision Tree Induction 66. Bayesian Classification 67. Naive Bayesian Classification 68. Bayesian Belief Networks 69. Training Bayesian Belief Networks 70. Using IF-THEN Rules for Classification 71. Rule Extraction from a Decision Tree 72. Rule Induction Using a Sequential Covering Algorithm 73. Rule Pruning 74. Introduction to Back propagation 75. A Multilayer Feed-Forward Neural Network 76. Defining a Network Topology 77. Support Vector Machines 78. Associative Classification: Classification by Association Rule Analysis 79. Evaluating the Accuracy of a Classifier or Predictor
Manufacturer: Ashish Kumar
Brand: Ashish Kumar
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