Data mining & Data Warehousing

About Data mining & Data Warehousing

The app is a complete free handbook of Data mining & Data Warehousing which cover important topics, notes, materials on the course.

This Data mining & Data Warehousing App lists 200 topics with detailed notes, diagrams, equations, formulas & course material, the topics are listed in 5 chapters. The app is must have for all the computer science & engineering students.

The App is designed for quick learning, revisions, references at the time of exams and interviews.

This app cover most of related topics and Detailed explanation with all the basics topics.

Some of the topics Covered in the data warehousing and data mining app are:

1. Introduction to Data mining
2. Data Architecture
3. Data-Warehouses (DW)
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 Data Warehouse 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

All topics are not listed because of character limitations.

Features :
* Chapter wise complete Topics
* Rich UI Layout
* Comfortable Read Mode
* Important Exam Topics
* Very simple User Interface
* Cover Most Of Topics
* One click get related All Book
* Mobile Optimized Content
* Mobile Optimized Images

This app will useful for quick reference. The revision of all concepts can be finished within Several hour using this app.

Data mining & Data Warehousing is part of computer science, software engineering, AI, Machine learning & Statistical Computing education course and information technology & business management degree programs at various universities.

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