By Piero Giacomelli
A quickly, clean, developer-oriented dive into the area of Mahout
- Learn how you can organize a Mahout improvement environment
- Start checking out Mahout in a standalone Hadoop cluster
- Learn to discover inventory industry path utilizing logistic regression
- Over 35 recipes with real-world examples to aid either expert and the non-skilled builders get the dangle of different gains of Mahout
The upward push of the web and social networks has created a brand new call for for software program which may study huge datasets that could scale as much as 10 billion rows. Apache Hadoop has been created to address such heavy computational projects. Mahout won attractiveness for offering facts mining class algorithms that may be used with such type of datasets.
"Apache Mahout Cookbook" offers a clean, scope-oriented method of the Mahout international for either novices in addition to complex clients. The booklet supplies an perception on tips on how to write various info mining algorithms for use within the Hadoop setting and select the easiest one suiting the duty in hand.
"Apache Mahout Cookbook" seems to be on the numerous Mahout algorithms to be had, and offers the reader a clean solution-centered procedure on how one can resolve various facts mining initiatives. The recipes begin effortless yet get steadily complex. A step by step method will consultant the developer within the diversified initiatives desirous about mining a massive dataset. additionally, you will how one can code your Mahout’s information mining set of rules to figure out the simplest one for a selected activity. Coupled with this, a complete bankruptcy is devoted to loading information into Mahout from an exterior RDMS process. loads of realization has additionally been wear utilizing your info mining set of rules within your code for you to manage to use it in an Hadoop setting. Theoretical features of the algorithms are lined for info reasons, yet each bankruptcy is written to permit the developer to get into the code as speedy and easily as attainable. which means with each recipe, the ebook offers the code for reusing it utilizing Maven in addition to the Maven Mahout resource code.
By the top of this booklet it is possible for you to to code your method to do numerous information mining initiatives with various algorithms and to guage and select the simplest ones to your tasks.
What you are going to examine from this book
- Configure from scratch a whole improvement setting for Mahout with NetBeans and Maven
- Handle sequencefiles for larger performance
- Query and shop effects into an RDBMS process with SQOOP
- Use logistic regression to foretell the following step
- Understand textual content mining of uncooked info with Naïve Bayes
- Create and comprehend clusters
- Customize Mahout to guage diverse cluster algorithms
- Use the mapreduce method of resolve actual global facts mining problems
"Apache Mahout Cookbook" makes use of over 35 recipes filled with illustrations and real-world examples to assist novices in addition to complex programmers get conversant in the positive factors of Mahout.
Who this publication is written for
"Apache Mahout Cookbook" is superb for builders who are looking to have a clean and speedy creation to Mahout coding. No earlier wisdom of Mahout is needed, or even expert builders or approach directors will enjoy the a variety of recipes presented.
Read or Download Apache Mahout Cookbook PDF
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Extra resources for Apache Mahout Cookbook
Dat file, which is the one that will be used, you should see the following lines: UserID::MovieID::Vote::datetime 1::1193::5::978300760 1::661::3::978302109 1::914::3::978301968 For every line you have a movie rating that can be interpreted as follows: user 1 gave a vote of 5 (out of 5) to the movie One Flew Over the Cuckoo's Nest and gave a vote of 3 to James and the Giant Peach and to My Fair Lady. The last long number is the long date/time of the rating itself. 19 Mahout is Not So Difficult!
Slopeone. 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.
4. Test whether everything works fine.