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Data Mining

5+ Years of Experience


  • Lessons : 12
  • Length : 6 Months
  • Level : Basic
  • Category : Software Training
  • Started : 04-04-2019
  • Shift : 02
  • Class : 120

Data Mining Training and Course Description

We are the best providers of Data Mining Training in Chennai with excellent placements. Our training program is very much mixed both practical and interview point of questions. It will helpful for the companies and students who are willing to learn Data Mining with live projects. We already build a perfect solution of Data Mining Placements exclusively for our students. We always schedule a demo class for each course for the new inquired students. It is completely FREE. It takes your 45mins of time. Once you attended the demo session you got the idea about what you are going to learn in Data Mining with us. Simply, we are the best Data Mining Training Institute in Chennai where you can learn the technology to core.

Our Data Mining Syllabus is crafted by many MNC HR’s and Experts which already satisfied many of the corporate. We are also having a separate dynamic division for client projects. By placement, course syllabus and practicals we are the BEST DATA Mining TRAINING INSTITUTE IN CHENNAI.

Data Mining Training and Course Syllabus

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