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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.

John Wiley Sons.