PDF Ebook Instant Weka How-to, by Boštjan Kaluža
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Instant Weka How-to, by Boštjan Kaluža
PDF Ebook Instant Weka How-to, by Boštjan Kaluža
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Implement cutting-edge data mining aspects in Weka to your applications
Overview
- 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
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.
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
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 written 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.
- Sales Rank: #1859888 in Books
- Published on: 2013-06-21
- Released on: 2013-06-21
- Original language: English
- Dimensions: 9.25" h x .19" w x 7.50" l,
- Binding: Paperback
- 80 pages
About the Author
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.
Most helpful customer reviews
4 of 4 people found the following review helpful.
Review
By Lucian
Weka is one of the most popular software used for machine learning experiments, in Java world; the software is introduced by "Data Mining: Practical Machine Learning Tools and Techniques". There are two approaches for using Weka: one is through the GUI and another one by programmatically calling the API. The book "Instant Weka how-to" targets programmers willing to integrate Weka inside their programs. It is written by a researcher with a proven experience in this area. In only 80 pages, the reader is shown some practical examples on how to embed Weka ML functionalities in Java applications.
What I like here is the brevity of exposition: for each example, the aims are clearly delineated; the main piece of code - together with imported packages - is shown as a whole block at first and then it is split into functional pieces.
The book starts with showing how to add reference to Weka jar into a Eclipse Java project (here the alternative of importing Weka library through Maven is unfortunately missing , but manually adding jar is a quicker approach for first examples). The programmer is then shown how to load an arff file, how to apply some preprocessing steps (in Weka parlance: filters), training a classifier, adding custom classifier, which is a plus), how to test and evaluate model through k-fold cross validation, how to produce confusion matrix and graphical representation of ROC curve, regression models, association rules, clustering and cluster evaluation, and (de)serialization of the models, among others.
Finally three practical DM problems are solved: classification (predicting buyer/non buyer), stock value forecasting and building a recommendation system.
Some more complex examples would make this books more appealing, e.g. applying a chain of filters (multifilter functionality from Weka),using cross validation for parameter selection, adding more details on how to create the plugin package.
The books fulfills the target stated by its title: a Java programmer can quickly embed Weka models inside his/her own code.
In a nut shell, the code snippets are clear, with no unnecessary burden, and the material presented is well delivered. In my opinion, the books is worth the price.
3 of 3 people found the following review helpful.
quick introduction to Weka programmable features
By filippo
If you are already familiar with Weka and you are looking for a quick introductory guide about its programmatic use, then this book would definitely be useful. It illustrates simple examples about pre-processing, classification, association rules, clustering and recommendation systems.
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