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Apr 09, 2013
2.2 and up
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Data Mining & Data Warehousing


Data Mining & Data Warehousing Description

This unique 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 PredictorAll topics not listed due to character limitations.

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