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Data Mining

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Pankha Road, Delhi

Course ID: 26625

Pankha Road, Delhi

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Rajesh Ranjan picture
Rajesh Ranjan

M.Tech (Computer Science Engineering)

7 Years of Experience

About Rajesh Ranjan

Having more than 10 years of Teaching Experience of Computer Science.
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About the Course

Data Mining Techniques for Students enrolled in B.Tech/M.Tech /MCA.

It will be conducted at our Centre Located in Janakpuri.

Topics Covered

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
Applications
Example: weather data
Data Warehouse and OLAP
Data Warehouse and DBMS
Multidimensional data model
OLAP operations
Example: loan data set
Data preprocessing
Data cleaning
Data transformation
Data reduction
Discretization and generating concept hierarchies
Installing Weka 3 Data Mining System
Experiments with Weka - filters, discretization
Data mining knowledge representation
Task relevant data
Background knowledge
Interestingness measures
Representing input data and output knowledge
Visualization techniques
Experiments with Weka - visualization
Attribute-oriented analysis
Attribute generalization
Attribute relevance
Class comparison
Statistical measures
Experiments with Weka - using filters and statistics
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
Data mining algorithms: Classification
Basic learning/mining tasks
Inferring rudimentary rules: 1R algorithm
Decision trees
Covering rules
Experiments with Weka - decision trees, rules
Data mining algorithms: Prediction
The prediction task
Statistical (Bayesian) classification
Bayesian networks
Instance-based methods (nearest neighbor)
Linear models
Experiments with Weka - Prediction
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
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.
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
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

Who should attend

Anyone

Pre-requisites

Nothing

What you need to bring

Notes and Laptops.

Key Takeaways

Notes.

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