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Data Mining

8 Reviews

Pankha Road, Delhi

Course ID: 26625

Pankha Road, Delhi

Students Interested 0 (Seats Left 0)


8 Reviews

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
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.
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




What you need to bring

Notes and Laptops.

Key Takeaways


Date and Time

Not decided yet.

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About the Trainer

Rajesh Ranjan picture

5 Avg Rating

149 Reviews

186 Students

9 Courses

Rajesh Ranjan

M.Tech (Computer Science Engineering)

7 Years of Experience

Having more than 10 years of Teaching Experience of Computer Science.

Student Feedback


Average Rating





I like his teaching methodology.. he is smart and intelligent.


Sujit Sonam

He is the best teacher I had this semester.


Anant Kumar

I am very much lucky that i met Rajesh Sir. Smoothly completed my degree.


Shyam Sharan

Rajesh Sir Is extremely talented and experienced. I got Good marks in even Subjects like Microprocessors.


Shruti Singh

I strongly recommend Mr. Ranjan, awesome teaching methodology.


Solanki J

I cleared my MCA backlogs with help of rajesh sir. I recommend him.

Sunder C

He is the best, i got placed in TCS becoz of Rajesh Sir,. Thanx sir.

Shalini Kumari

He is always good as a Teacher. one word " Awsome Teaching".


Rajesh Ranjan picture

Rajesh Ranjan


8 Reviews

Students Interested 0 (Seats Left 0)

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