Data mining & Data Warehousing Pro: Application description
The app is a complete 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.