The document discusses data mining and its evolution from early database systems. It defines data mining as a process of finding patterns in data through techniques like forecasting, classification, clustering, association, and sequencing. The document outlines the standard phases of data mining including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It provides examples of how organizations use data mining for applications such as fraud detection, customer profiling, and targeting.

1. Ahmed Moussa30-Feb-2010

2. What is Data Mining?

3. The Evolution of Data Analysis

4. - - RDBMS: A relational database management system- ODBC: Open Database Connectivity (ODBC) provides a standard software API method for using database management systems (DBMS). - OLAP: Online analytical processing, is an approach to quickly answer multi-dimensional analytical queries.- SPSS: Statistical Package for the Social Sciences (formerly SPSS) is a computer program used for statistical analysis. Before 2009 it was called SPSS, but in 2009 it was re-branded as PASW.

5. Results of Data Mining Include

6. Results of Data Mining IncludeForecasting what may happen in the future

7. Classifying people or things into groups by recognizing patterns.

8. Clustering people or things into groups based on their attributes.

9. Associating what events are likely to occur together.

10. Sequencing what events are likely to lead to later events.Results of Data Mining IncludeForecasting what may happen in the future

11. Classifying people or things into groups by recognizing patterns.

12. Clustering people or things into groups based on their attributes.

13. Associating what events are likely to occur together.

14. Sequencing what events are likely to lead to later events.Results of Data Mining IncludeForecasting what may happen in the future

15. Classifying people or things into groups by recognizing patterns.

16. Clustering people or things into groups based on their attributes.

17. Associating what events are likely to occur together.

18. Sequencing what events are likely to lead to later events.Results of Data Mining IncludeForecasting what may happen in the future

19. Classifying people or things into groups by recognizing patterns.

20. Clustering people or things into groups based on their attributes.

21. Associating what events are likely to occur together.

22. Sequencing what events are likely to lead to later events.Results of Data Mining IncludeForecasting what may happen in the future

23. Classifying people or things into groups by recognizing patterns.

24. Clustering people or things into groups based on their attributes.

25. Associating what events are likely to occur together.

26. Sequencing what events are likely to lead to later events.Data mining is notCrunching of bulk data

27. “Blind” application of algorithms

28. Going to find relationships where none exist

29. Presenting data in different ways

30. A database intensive task

31. A difficult to understand technology requiring an advanced degree in computer scienceData Mining IsA class of techniques that find patterns in data.

32. A user-centric, interactive process which leverages analysis technologies and computing power.

33. A group of techniques that find relationships that have not previously been discovered.

34. Not reliant on an existing database.

35. A relatively easy task that requires knowledge of the business problem/subject matter expertise.Data Mining IsA class of techniques that find patterns in data.

36. A user-centric, interactive process which leverages analysis technologies and computing power.

37. A group of techniques that find relationships that have not previously been discovered.

38. Not reliant on an existing database.

39. A relatively easy task that requires knowledge of the business problem/subject matter expertise.Data Mining IsA class of techniques that find patterns in data.

40. A user-centric, interactive process which leverages analysis technologies and computing power.

41. A group of techniques that find relationships that have not previously been discovered.

42. Not reliant on an existing database.

43. A relatively easy task that requires knowledge of the business problem/subject matter expertise.Data Mining IsA class of techniques that find patterns in data.

44. A user-centric, interactive process which leverages analysis technologies and computing power.

45. A group of techniques that find relationships that have not previously been discovered.

46. Not reliant on an existing database.

47. A relatively easy task that requires knowledge of the business problem/subject matter expertise.Data Mining IsA class of techniques that find patterns in data.

48. A user-centric, interactive process which leverages analysis technologies and computing power.

49. A group of techniques that find relationships that have not previously been discovered.

50. Not reliant on an existing database.