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Data Mining


The workshop is practical in nature. All contents are presented in the context of using WEKA and other tools, the most widely-used open source machine learning software in the world.

The workshop includes hands-on exercises using WEKA and other tools and data sets to do actual data mining. The course assumes no prior knowledge; but it is not a trivial treatment. It covers the basics and advances of machine learning, and surveys a majority of the most commonly used algorithms.
This is a practical course with design exercises. Participants are required to bring their laptops to the course.

Topics to be covered:

1. Introduction to Data Mining
What is data mining?
Related technologies - Machine Learning, DBMS, OLAP, Statistics
Data Mining Goals
Stages of the Data Mining Process
Data Mining Techniques
Knowledge Representation Methods
Example: weather data

2. Data Warehouse and OLAP
Data Warehouse and DBMS
Multidimensional data model
OLAP operations
Example: loan data set

3. Data preprocessing
Data cleaning
Data transformation
Data reduction
Discretization and generating concept hierarchies
Installing Weka 3 Data Mining System
Experiments with Weka - filters, discretization

4. Data mining knowledge representation
Task relevant data
Background knowledge
Interestingness measures
Representing input data and output knowledge
Visualization techniques
Experiments with Weka - visualization

5. Attribute-oriented analysis
Attribute generalization
Attribute relevance
Class comparison
Statistical measures
Experiments with Weka - using filters and statistics

6. Data mining algorithms: Association rules
Motivation and terminology
Example: mining weather data
Basic idea: item sets
Generating item sets and rules efficiently
Correlation analysis
Experiments with Weka - mining association rules

7. Data mining algorithms: Classification
Basic learning/mining tasks
Inferring rudimentary rules: 1R algorithm
Decision trees
Covering rules
Experiments with Weka - decision trees, rules

8. Data mining algorithms: Prediction
The prediction task
Statistical (Bayesian) classification
Bayesian networks
Instance-based methods (nearest neighbor)
Linear models
Experiments with Weka - Prediction

9. Evaluating what\'s been learned
Basic issues
Training and testing
Estimating classifier accuracy (holdout, cross-validation, leave-one-out)
Combining multiple models (bagging, boosting, stacking)
Minimum Description Length Principle (MLD)
Experiments with Weka - training and testing

10. Mining real data
Preprocessing data from a real medical domain (310 patients with Hepatitis C).
Applying various data mining techniques to create a comprehensive and accurate model of the data.

11. Clustering
Basic issues in clustering
First conceptual clustering system: Cluster/2
Partitioning methods: k-means, expectation maximization (EM)
Hierarchical methods: distance-based agglomerative and divisible clustering
Conceptual clustering: Cobweb
Experiments with Weka - k-means, EM, Cobweb

12. Advanced techniques, Data Mining software and applications
Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
Bayesian approach to classifying text
Web mining: classifying web pages, extracting knowledge from the web
Data Mining software and applications.

Hardware Kit: This workshop does not include any hardware kit.

- A working Laptop/PC with minimum of 1 GB RAM, 100 GB HDD, intel i3+ processor
- A Seminar Hall with sitting capacity of all participants along with charging plugs, proper ventilation
- Projector, Collar Mike and Speakers

- Digital toolkit of PPTs and study material for all participants
- Certificate of Participation for every participant.
- A competition will be organized at the end of the workshop and winners will be awarded by Certificate of Excellence.

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