【實用教育app】Data mining&Data Warehousing 1|最夯免費app

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

All topics not listed due to character limitations.

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