Data mining & Data Warehousing

The app is a complete free handbook of Data mining & Data Warehousing which cover important topics, notes, materials, news & blogs on the course. Download the App as a reference material & digital book for computer science, AI, data science & software engineering programs & business management degree courses. 

This useful 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 & professionals. 

The app provides quick revision and reference to the important topics like a detailed flash card notes, it makes it easy & useful for the student or a professional to cover the course syllabus quickly before an exams or interview for jobs. 

Track your learning, set reminders, edit the study material, add favorite topics, share the topics on social media. 

You can also blog about engineering technology, innovation, engineering startups,  college research work, institute updates, Informative links on course materials & education programs from your smartphone or tablet or at http://www.engineeringapps.net/. 

Use this useful engineering app as your tutorial, digital book, a reference guide for syllabus, course material, project work, sharing your views on the blog. 

Some of the topics Covered in the 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 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

Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding. 

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. </br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br></br>