This document provides an overview of data mining and the Orange software tool for data mining. It defines data mining as the process of analyzing data from different perspectives to summarize it into useful information. It then discusses major data mining tasks like classification, clustering, deviation detection, and forecasting. It also introduces the concepts of data warehouses and decision trees. The document proceeds to describe Orange, an open-source software for visual data mining and analytics. Orange contains various widgets that can be used for data preprocessing, visualization, and machine learning algorithms. Finally, the document demonstrates some Orange widgets and provides references for further information.

1. Data Mining Tool
Neeraj Goswami

2. Contents
• Data mining
• Data warehouse
• Orange Software
• Orange Widgets
• Demo

3. What is Data Mining ?
• process of analyzing
data from different
perspectives
• summarizing it into
useful information
• information that can be
used to increase
revenue, cuts costs, or
both.

4. Analysis(cont…)
Data mining helps analysts recognize significant
• facts
• relationships
• trends
• patterns
• Exceptions
• anomalies
that might otherwise go unnoticed.

5. Industries Using Data Mining
• retail
• finance
• heath care
• manufacturing transportation
• aerospace

6. Major Data Mining Tasks
1)Classification: Predicting an item class
2)Clustering: descriptive, finding groups of
items
3)Deviation Detection: predictive, finding
changes
4)Forecasting: predicting a parameter value
5)Description: describing a group
6)Link analysis: finding relationships and
associations

7. Data Warehouse
A single, complete and
consistent store of data
obtained from a variety of
different sources made
available to end users in a
what they can understand
and use in a business
context.

8. Data Warehouse-Layers

9. Decision Tree(classification algo.)
20 No Low
25 Yes High
44 Yes High
18 No Low
55 No High
35 No Low
Smoke
Age
Yes No
0-35 36 - 100
Insurance
Risk
High
High
Low
Age Smoke Risk

10. Decision tree advantages
• Its model is simple to understand and
interpret
• Requires little data preparation
• Possible to validate a model using
statistical tests.
• Robust

11. ORANGE SOFTWARE
 Open source
 Component based
 data visualization
 analysis for novice and
experts.
 Data mining through visual
programming or Python
scripting.
 Add-ons for bioinformatics
and text mining.
 Packed with features for data
analytics.

12. Orange Developments
• In1997-developed in Bioinformatics Laboratory
of the Faculty of Computer and
Information Science, Slovenia.
• In 2005- extents data analysis
in bioinformatics
• In 2008- installation packages were developed.
• In 2009- over 100 widgets were created and
maintained.

13. Widgets ?
• Orange widgets provide a graphical user’s
interface to Orange’s data mining and
machine learning methods. They include
widgets for
• data entry and preprocessing
• data visualization,
• Classification

14. Data Widget

15. Classify Widget

16. Examples
• Any of your schemas
should probably start
with the file widget. In
the schema below, the
widget is used to read
the data that is then
sent to both data
table widget and to
widget that
displays attributes
statistics.

17. Scatter Plot( a widget)

18. Scripting

19. Visualization

20. DEMO

21. References
– http://orange.biolab.si/docs/latest/
– http://en.wikipedia.org/wiki/Data_mining
– http://www.oracle.com/technetwork/database/o
ptions/advanced-analytics/odm/index.html
– http://orange.biolab.si/features/
– http://en.wikipedia.org/wiki/Orange_(software)
– http://eprints.fri.uni-lj.si/1150/1/DataMining-
Kyoto.pdf

22. THANK YOU

23. Questions
????