Instant Weka How-to [Instant]


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Instant Weka How-to [Instant]
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Learn in an Instant - Short, Fast, Focused
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  • Learn something new in an Instant! A short, fast, focused guide delivering immediate results
  • A practical guide with examples and applications of programming Weka in Java
  • Start with the basics and dive deeper into the more advanced aspects of Weka
  • Learn how to include Weka’s machinery in your Java application

Book Details

Language : English
eBook : 80 pages
Release Date : June 2013
ISBN : 1782163867
ISBN 13 : 9781782163862
Author(s) : Boštjan Kaluža
Topics and Technologies : All Books, Big Data and Business Intelligence, Instant, Java, Open Source

Table of Contents

Preface
Instant Weka How-to
  • Instant Weka How-to
    • Starting with Java and Weka (Simple)
    • Loading the data (Simple)
    • Filtering attributes (Simple)
    • Selecting attributes (Intermediate)
    • Training a classifier (Simple)
    • Building your own classifier (Advanced)
    • Tree visualization (Intermediate)
    • Testing and evaluating your models (Simple)
    • Regression models (Simple)
    • Association rules (Intermediate)
    • Clustering (Simple)
    • Reusing models (Intermediate)
    • Data mining in direct marketing (Simple)
    • Using Weka for stock value forecasting (Advanced)
    • Recommendation system (Advanced)

Boštjan Kaluža

Boštjan Kaluža, PhD is a researcher in artificial intelligence and ubiquitous computing. Since October 2008, he has been working at Jozef Stefan Institute, Slovenia. His research focuses on the development of novel algorithms and approaches, with an emphasis on human behavior analysis from sensor data using machine learning and data mining techniques. Boštjan has extensive experience in Java and Python, and lectures Weka in the classroom. He spent a year as a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications. He has published over 40 journal articles and conference papers.

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What you will learn from this book

  • Start to code with Weka and Java
  • Load the data
  • Select and filter attributes
  • Train a classifier
  • Create your own classifier
  • Test and evaluate your models
  • Construct regression models
  • Build association rules and clusters
  • Predict house value
  • Implement direct marketing applications

In Detail

Data mining has become one of the hottest topics in computer science, mainly due to the vast amounts of data in diverse applications such as market basket analysis, reactive business intelligence, human genome sequence mining, speech recognition, document search, and spam detection.

Instant Weka How-to shows you exactly how to include Weka’s machinery in your Java application to stay ahead by implementing cutting-edge data-mining aspects such as regression and classification, and then moving on to more advanced applications of forecasting, decision making, and recommendations.

This book shows you exactly how to include Weka’s machinery in your Java application. The book starts by importing and preparing the data, and then moves on to more serious topics on classification, regression, clustering, and evaluation. For those of you who are eager to dive deeper, this book shows you how to implement online learning or how to create your own classifier. The book includes several application examples such as house price prediction, stock value forecasting, and decision making for direct marketing.

Approach

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. A practical guide with examples and applications of programming Weka in Java.

Who this book is for

This book primarily targets Java developers who want to build Weka’s data mining capabilities into their projects. Computer science students, data scientists, artificial intelligence programmers, and statistical programmers would equally gain from this book and would learn about essential tasks required to implement a project. Experience with Weka concepts is assumed.

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