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IT Training - Data Mining PDF Print E-mail
Written by Administrator   
Monday, 20 January 2014 09:33

Data Mining and WEKA

  • Data Mining.
  • Machine Learning
  • Supervised and Unsupervised Learning.
  • Data Mining Algorithms.
  • WEKA as machine learning application.
  • Dataset Sample.

Weka Application and Component

  • Installation.
  • Weka Components
  • Explorer
  • Experimenter
  • KnowledgeFlow
  • Simple CLI

Preprocess (Data Preparation)

  • Weka Explorer „s Preprocess
  • Dataset, Instance, Attribute, Class Attribute and Data Types
  • Data Input Format
  • ARFF (Attribute-Relation File Format)
  • CSV (Comma Separated Value)
  • Database Table?s Access
  • Data Population Statistics
  • Practice : Opening and Exploring ARFF Data
  • Practice : Data Transformation
  • Practice : MySQL Database Access
  • Filtering
  • Practice : Removing and Discretize Attributes Using Filter

Data Visualization

  • Visualize Data and Data Mining Results
  • Practice: Explore Data Visualization Types
  • Jitter to Help Visualize Nodes
  • Practice : Using Jitter with Heavily Populated Data
  • Missing Value
  • Data Mining Solution with WEKA
  • PHI-Integration.com Page : 5 / 5

Classification

  • What is Classsification ?
  • Classfication Types : Decision Tree
  • Decision Tree Algorithm : J48
  • Practice : Pattern classification using J48
  • User Classifier
  • Practical : Decision Tree dengan User Classifier
  • Exporting and Importing Object Model as Pattern Result

Association Rules

  • Association Rules to find sequence or dependency data.
  • Association Rule algorithm : Apriopri.
  • Practice : Using Apriori to solve market basket analysis problem.

Clustering

  • Finite categorization with Clustering.
  • What is K-Mean Clustering ?
  • K-Mean implementation using SimpleKMean algorithm.
  • Practice : Cluster heavily populated data with many attributes.

Experimenter

  • Experimenter to compare several Object Model predictions.
  • Object Model and experiment configurations.
  • Practice : Compare results on several object models from previous exercises.

Simple CLI

  • SimpleCLI and Java Command.
  • Benefits of using SimpleCLI.
  • Practice : Using SimpleCLI to generate Decision Tree Object Model. 

KnowledgeFlow

  • KnowledgeFlow as automation tool to do data acquisition, pattern recognition and producing prediction result.
  • Calling knowledgeflow from Java command line interface.
  • Practice : Design and calling knowledgeflow.

Biaya pendaftaran : Rp 100.000,-

Biaya pelatihan     : Rp 3.900.000,-

Durasi pelatihan     : 36 jam / 12x pertemuan @3 jam

Fasilitas                : Modul, sertifikat, makan siang, snack (khusus kelas malam), coffee break

Jadwal pelatihan   : 1. Setiap hari Sabtu / Minggu jam : 09.00-16.00 WIB

                             2. Setiap hari Senin-Rabu / Selasa-Kamis : jam 18.30-21.30 WIB

Tempat pelatihan dan Pendaftaran : Jl.Raya Pasar Minggu No.15D (Sebrang Asrama Brimob / Dirjen PMD ) Telp : 021-7900022 / 021-86341001


Last Updated on Tuesday, 05 January 2016 02:46