The document discusses the theory and practical applications of data mining, particularly using the R programming language. It covers data preparation, various data mining techniques such as regression, classification, clustering, and text mining, and emphasizes the goal of extracting useful information from large datasets. Additionally, it briefly mentions tools and platforms like R console and RHIPE for data mining processes.

1. R & data mining in action
Katarzyna Mrowca

2. Sztuka czytania między
wierszami
czyli język R i Data Mining w akcji

3. <me>
Katarzyna Mrowca
</me>

5. The deal 

6. Agenda
• Quick glance on theory - Data mining
• Exercises on… paper
• Quick glance on tool – R console
• Exercises – became friend with R
•…

7. Agenda
• Quick glance on theory - Data mining
• Exercises on… paper
• Quick glance on tool – R console
• Exercises – became friend with R
•…
Theory
Exercise

8. Agenda
• Quick glance on theory - Data preparation
• Exercises
• Regression
• Time series
• Decision trees
• Cluser analysis
Theory
• Text mining
•…
Exercise

9. Quick glance on theory!

10. What data mining is?

11. What „google” says?

12. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science,

13. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science, is the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial intelligence,
machine learning, statistics.

14. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science, is the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial intelligence,
machine learning, statistics.

15. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science, is the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial intelligence,
machine learning, statistics.

16. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science, is the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial intelligence,
machine learning, statistics.

17. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science, is the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial intelligence,
machine learning, statistics.

18. What „google” says?
The overall goal of the data mining process is to extract information
from a data set and transform it into an understandable structure for
further use.

19. What „google” says?
The overall goal of the data mining process is to extract information
from a data set and transform it into an understandable structure for
further use.

20. What „google” says?
The overall goal of the data mining process is to extract information
from a data set and transform it into an understandable structure for
further use.

21. What „google” says?
Aside from the raw analysis step, it involves database and data
management aspects, data pre-processing, model and inference
considerations, interestingness metrics, complexity considerations,
post-processing of discovered structures, visualization, and online
updating.
Source: wikipedia

22. Data mining – what is „inside”
• Predictive
• Regression
• Classification
• Collaborative Filtering
• Descriptive
• Clustering / similarity matching
• Association rules and variants
• Deviation detection

23. Data mining – what is „inside”
• Predictive:
• Regression
• Classification
• Collaborative Filtering
• Descriptive:
• Clustering / similarity matching
• Association rules and variants
• Deviation detection

24. Data mining – what is „inside”
• Predictive:
• Regression
• Classification
• Collaborative Filtering
• Descriptive:
• Clustering / similarity matching
• Association rules and variants
• Deviation detection

25. What data mining is not?

26. Why Data Mining is so
popular?

27. What is a difference between
statistics and data mining?

28. Data preparation

29. Variables

30. Qualitative & Quantitative

31. Tame R console!

32. NetBeans + R
Source: https://blogs.oracle.com/geertjan/entry/r_plugin_for_netbeans_ide

33. RHIPE <– R+ Hadoop
Find out more: http://www.datadr.org/

34. Revolution Analytics <- R +
Hadoop + Enterprise
Find out more: http://www.revolutionanalytics.com

35. Take a break 

36. Regression

37. Time series

38. Decision trees

39. Regression trees

40. Classification trees

41. K means

42. Text mining

43. Thank you!