By Piero Giacomelli
A speedy, clean, developer-oriented dive into the area of Mahout
- Learn tips to arrange a Mahout improvement environment
- Start checking out Mahout in a standalone Hadoop cluster
- Learn to discover inventory marketplace path utilizing logistic regression
- Over 35 recipes with real-world examples to assist either expert and the non-skilled builders get the dangle of the various beneficial properties of Mahout
The upward push of the web and social networks has created a brand new call for for software program which can study huge datasets that may scale as much as 10 billion rows. Apache Hadoop has been created to deal with such heavy computational projects. Mahout received reputation for delivering facts mining category algorithms that may be used with such form of datasets.
"Apache Mahout Cookbook" presents a clean, scope-oriented method of the Mahout international for either novices in addition to complicated clients. The publication supplies an perception on how you can write varied information mining algorithms for use within the Hadoop surroundings and select the easiest one suiting the duty in hand.
"Apache Mahout Cookbook" seems on the numerous Mahout algorithms to be had, and offers the reader a clean solution-centered procedure on find out how to remedy various info mining projects. The recipes commence effortless yet get steadily complex. A step by step process will advisor the developer within the diversified initiatives keen on mining a big dataset. additionally, you will find out how to code your Mahout’s information mining set of rules to figure out the simplest one for a selected activity. Coupled with this, an entire bankruptcy is devoted to loading info into Mahout from an exterior RDMS procedure. loads of recognition has additionally been wear utilizing your facts mining set of rules within your code with a purpose to manage to use it in an Hadoop surroundings. Theoretical facets of the algorithms are lined for info reasons, yet each bankruptcy is written to permit the developer to get into the code as quick and easily as attainable. which means with each recipe, the e-book presents the code for reusing it utilizing Maven in addition to the Maven Mahout resource code.
By the top of this publication it is possible for you to to code your approach to do a number of info mining initiatives with varied algorithms and to guage and select the simplest ones on your tasks.
What you'll research from this book
- Configure from scratch a whole improvement atmosphere for Mahout with NetBeans and Maven
- Handle sequencefiles for higher performance
- Query and shop effects into an RDBMS method with SQOOP
- Use logistic regression to foretell the following step
- Understand textual content mining of uncooked information with Naïve Bayes
- Create and comprehend clusters
- Customize Mahout to judge diversified cluster algorithms
- Use the mapreduce method of remedy genuine international facts mining problems
"Apache Mahout Cookbook" makes use of over 35 recipes full of illustrations and real-world examples to aid novices in addition to complicated programmers get accustomed to the beneficial properties of Mahout.
Who this booklet is written for
"Apache Mahout Cookbook" is excellent for builders who are looking to have a clean and quick creation to Mahout coding. No past wisdom of Mahout is needed, or even expert builders or procedure directors will enjoy the numerous recipes presented.
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Additional resources for Apache Mahout Cookbook
Write the key and the value pair to the sequence file. We open the file using the BufferedReader Java base object. class); 41 Using Sequence Files – When and Why? To create a sequence file you need to declare the Hadoop Configuration and FileSystem type, and the class of the key and value pair. In our case we use the predefined Hadoop classes, the LongWritable and Text classes, corresponding to the long and string types in Java. txt have the space as separator, we need to split and separate every line to find the artist's name.
Csv"; 22 Chapter 1 Moving to the main method and the core of our code we first code a method to transform the original MovieLens file into a csv file without the vote as explained before. close(); } } 2. Then, it is time to build the model based on the comma-separated value (CSV) file shown as follows: // create data source (model) - from the csv file File ratingsFile = new File(outputFile); DataModel model = new FileDataModel(ratingsFile); 23 Mahout is Not So Difficult! 3. nextLong(); 4. At the end, we simply display the result recommendation: // get the recommendations for the user List
Dat file: 3461::Lord of the Flies (1963)::Adventure|Drama|Thriller 2::Jumanji (1995)::Adventure|Children's|Fantasy It is interesting to notice that both the films' plots revolve around the adventures of children. So, it seems that the suggestion given is a good one considering the age of the user, even if the title of the movie Lord of the Flies could be updated. The reader could try to give a different run to the program by using 100, instead of using 10,000 ratings. This can be done by substituting the line, i < 10000, with i < 100.
Apache Mahout Cookbook by Piero Giacomelli