Product Description

Objective

Complete understanding of Java programing for Data mining. Learn How to use and customize data mining algorithms using Java.

We will start with core java. Basic features will be included with lots of practical example. Followed by advanced concepts for taking you to next level. As some of the algorithms have features of Java 8 , that also covered with examples.

Once finished with Java , Java Data Mining fundamentals will be covered in details. After all fundamentals of Java Data Mining , more practice will be done with lots of SPMF algorithms.

SPMF is a library completely written in Java.

To make it more advanced , weka and Rapidminer also included at practical level.

Outcome

At the end of this training, trainee will have complete knowledge of working with Java. Trainee will have complete practical knowledge of Advance Java along with Java 8.

Trainee will be able to work with Java data mining, Working with algorithms, and customizing it. Trainee will have enough time to have hands-on approach for different kinds of Data Mining algorithms. Trainee will also get knowledge of JAVA based Data Mining tools also.

Training will provide complete implementation of all Data Mining algorithms using JAVA. After finishing the training, trainee will be able to implement any data mining algorithm using Java and they will be able to use any tools related to JAVA data mining.

Duration: 25 Days

Course Content:
  • Overview of JAVA Basics

    • Data Type, Array, Methods, Class and Objects, Operators, Control Statements
  • Constructor

    • By default constructor
    • Constructor with arguments
  • Inheritance

    • Inheritance Basics

      • Member Access and Inheritance
      • A Superclass Variable Can Reference a Subclass Object
    • Using super
    • The this Keyword
    • Method Overriding
  • Abstract and Interface
  • Package Introduction
  • Exceptional Handling

    • Exception-Handling Fundamentals
    • Exception Types
    • Uncaught Exceptions
    • Using try and catch
    • Multiple catch Clauses
    • Nested try Statements
    • throw
    • throws
    • Finally
    • Chained Exceptions
  • String Handling

    • The String Constructors
    • String Length
    • String Concatenation
    • String Conversion and toString( )
    • Character Extraction

      • charAt( )
      • getChars( )
      • getBytes( )
      • toCharArray( )
    • String Comparison

      • equals( ) and equalsIgnoreCase( )
      • regionMatches( )
      • startsWith( ) and endsWith( )
      • equals( ) Versus ==
      • compareTo( )
    • Modifying a String

      • substring( )
      • concat( )
      • replace( )
      • trim( )
    • String Vs StringBuffer
  • Collection Framework

    • Collections Overview
    • The Collection Interfaces

      • The Collection Interface
      • The List Interface
      • The Set Interface
      • The SortedSet Interface
    • The Collection Classes

      • The ArrayList Class
      • The LinkedList Class
      • The HashSet Class
      • The LinkedHashSet Class
      • The TreeSet Class
    • Accessing a Collection via an Iterator
    • Working with Maps Interfaces and classes
    • Using a Comparator
  • Java IO Stream

    • I/O Basics
    • Streams
    • Byte Streams and Character Streams
    • The Predefined Streams
    • Reading Console Input
    • Reading Characters
    • Reading Strings
    • Writing Console Output
    • Reading and Writing Files
  • Simple Type Wrappers

    • Number
    • Double and Float
    • Byte, Short, Integer, and Long
    • Character
    • Boolean
  • Generics

    • Advantage of Java Generics
    • Generic for collection
    • Generic for class
    • Generic for methods
  • JDBC

    • SQL Syntax
    • Driver Types
    • Connection
    • Statement
    • Transaction
  • Multi thread

    • The Java Thread Model
    • Thread Priorities
    • Synchronization
    • The Thread Class and the Runnable Interface
    • Creating a Thread
    • Creating Multiple Threads
    • Synchronization
    • Using isAlive( ) and join( )
  • Java 8 New Feature : Lambda Expression for inbuilt interfaces and user defined interfaces
  • Java 8 New Feature: Date time API

    • Local Date Time and Zoned Date Time
    • Chrono Units
    • Period & Duration
    • Temporal Adjusters
  • Java 8 New Feature : Stream API

    • What is Stream?
    • Generating Streams
    • map
    • Filter
    • Limit
    • Sorted
    • Parallel Processing
    • Collectors
    • Statistics
  • Overview Of Data Mining Process

    • Introduction to data mining
    • Value of Data Mining
  • What is JDM strategy
  • Role of standards

    • Why to create a standard
    • What do data mining standards enable ?
  • Java Data mining Concepts

    • Classification problem
    • Regression Problem
    • Attribute Importance
    • Association rule problem
    • Clustering Problem
  • Design of JDM Api

    • Object modeling of Data mining concepts
    • Modular Package
    • Connection Architecture
    • Object Factories
    • Uniform resource identifier for data sets
    • Enumerated types
  • Using JDM Api

    • Connection Interfaces
    • Using JDM Enumeration
    • Using Data specification Interfaces
    • Using Classification Interfaces
    • Using Regression Interfaces
    • Using Attribute Importance Interfaces
    • Using Association Interfaces
    • Using Clustering Interfaces
  • XML Schema

    • Overview
    • Schema Elements
    • Schema types
    • Using PMML with JDM Schema
    • Use cases for JDM XML schema and documents
  • Web Services

    • What is web service
    • Service Oriented Architecture
    • JDM Web service
    • A Web service client in Java
  • Building Data mining tool using JDM

    • Data mining tools
    • Administrative console
    • User interface to save and build and save a model
    • User interface to test model Quality
  • How to design memory-efficient data mining algorithms in Java?
  • Introduction to Rapidminer
  • Fundamental Terms:

    • Attributes
    • Attribute Roles
    • Value Roles
    • Data and metadata
    • Modelling
  • Installation of Rapidminer
  • Perspective and views:
  • Design Perspective:

    • Operators and Repositories view
    • Process View
    • Operators and Processes
    • Parameters view
    • Help and comment view
    • Overview view
    • Problems and Log view
  • Design of Analysis process

    • New process
    • Repository Actions
    • Analysis process

      • Transforming metadata
    • Executing Processes

      • Looking up results
      • Breakpoints
    • Data and Result Visualization

      • Result Visualization
      • Data copies and Views
      • Display Formats

        • Description
        • Tables
        • Charts
        • Graphs
  • Repository

    • Using RapidMiner Studio Repository
  • Using Repository:

    • Processes and Relative Repository Descriptions
    • Importing Data and Objects into the Repository
    • Access to and Administration of the Repository
    • The Process Context
  • Data and Meta Data

    • Propagating Meta Data from the Repository and through the Process
  • Introduction to Weka
  • A command-line primer
  • GUI :

    • Launching Weka.
    • Package Manager
    • Simple CLI
    • Explorer
    • Experimenter
    • Knowledge Flow
  • Data

    • ARFF

      • Overview
      • Examples
      • ARFF Files
    • XRFF

      • File Extension
      • Comparison of ARFF and XRFF
      • Sparse Format
    • Converters

      • Introduction
      • File converter
      • Data base converter
  • Data

      • Stemmers

        • Show ball Stemmers
        • Using Stemmers
        • Adding new Stemmers
      • Databases

        • Configuration file
        • Setup
        • Missing Data types
        • Stored Procedures
  • Sequential Pattern Mining

    • SPMF Algorithms: The CM-SPADE algorithm
    • SPMF Algorithms: The ClaSP algorithm
    • SPMF Algorithms: The VMSP algorithm
    • SPMF Algorithms: The TKS algorithm
    • SPMF Algorithms: The VGEN algorithm
  • Sequential Rule Mining

    • SPMF Algorithms: The ERMiner algorithm
    • SPMF Algorithms: The RuleGrowth algorithm
    • SPMF Algorithms: The CMRules algorithm
    • SPMF Algorithms: The TRuleGrowth algorithm
  • Sequence Prediction

    • SPMF Algorithms: The Compact Prediction Tree (CPT) algorithm
    • The First order Markov Chains (PPM)
    • The Dependency Graph (DG)
  • High-Utility Pattern Mining

    • SPMF Algorithms: The EFIM algorithm
    • SPMF Algorithms: The FHM+ algorithm
    • SPMF Algorithms: The FCHM algorithm
    • SPMF Algorithms: The FHN algorithm
  • Clustering

    • SPMF Algorithms: The original K-Means algorithm
    • SPMF Algorithms: The Bisecting K-Means algorithm
    • SPMF Algorithms for density-based clustering

      • The DBScan algorithm
      • The Optics algorithm to extract a cluster ordering of points, which can then be used to generate DBScan style clusters and more
    • A hierarchical clustering algorithm
  • Classification

    • Supervised learning
    • Unsupervised learning
    • Decision tree learning
    • SPMF Algorithms: The ID3 algorithm for building decision trees
  • Association Rule Mining

    • SPMF Algorithms: An algorithm for mining all association rules from a transaction database
    • SPMF Algorithms: An algorithm for mining The IGB informative and generic basis of association rules from a transaction database
    • SPMF Algorithms: An algorithm for mining perfectly sporadic association rules
    • SPMF Algorithms: An algorithm for mining closed association rules
  • Frequent Itemset Mining

    • SPMF Algorithms: The Apriori algorithm
    • SPMF Algorithms: The FP-Growth algorithm
    • SPMF Algorithms: The Eclat algorithm
  • Learn and use Keel Data Processing Tool
  • Introduction to Keel
  • Data management

    • Data Import
    • Database SQL to keel
    • Data Export
  • File Format

    • XML data file format
    • HTML data file format
    • KEEL data file format
  • Visualize Data
  • Edit data
  • Data Partition

    • K-fold cross validation
    • 5×2 cross validation
    • K-Fold Distribution Optimality Balanced Stratified Cross Validation
  • Experiment design: Off –Line Module
  • Experiment graph

    • Preprocessing Methods
    • Data complexity
    • Discretization
    • Evolutionary feature selection
    • Evolutionary training selection
    • Feature selection
    • Missing values
    • Noisy data filtering
    • Training set Selection
    • Transformation
    • Statistical tests
    • Test for classification
    • Test for regression
    • Visualization modules
    • Show results (classification )
    • Show results (Regression)
    • Multiple results (classification)
    • Multiple results(Regression)
    • Fingrams(Fuzzy)
  • Algorithm parameters configuration
  • Generation of experiments

    • exe directory
    • scripts directory
    • datasets directory
    • results directory
  • Running KEEL Experiments
  • KEEL Modules

    • Imbalanced Learning Module
    • Statistical tests Module
    • Semi-supervised Learning Module
    • Multiple Instance Learning Module
  • Social Network Analysis Tool

    • Introduction to JUNG (It is written Java Based Social Network Analysis Tool)
    • JUNG Graph Basics
    • Working with Algorithms
    • User Data
    • Working with Input Output
    • Apply Filtering