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Instant Weka How-to
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.
80 pages, Kindle Edition
First published January 1, 2013
2 people are currently reading
4 people want to read
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Displaying 1 - 2 of 2 reviews
July 20, 2013
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.
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.
August 14, 2013
Weka is undoubtedly the best open source Machine Learning library written in Java. Most people are comfortable using Weka as an easy to use GUI tool for their data analysis tasks. However, the Weka libraries can be also used from Java code by importing appropriate JAR files. There are very limited material available across the web in this regard. This book "Instant Weka How-To" delves deep into explaining different possibilities of using Weka libraries through Java code by providing examples. The examples covered in this book encompass the areas of classification, clustering, association rule mining, recommendation systems etc. This is a very good beginners guide for anybody with Java knowledge to get started with data analysis tasks without any hassles. I strongly recommend this book for wannabe data scientists, who likes to do hands-on experiments with data .
Displaying 1 - 2 of 2 reviews