By Piero Giacomelli
A speedy, clean, developer-oriented dive into the realm of Mahout
- Learn easy methods to organize 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 different positive factors of Mahout
The upward thrust of the net and social networks has created a brand new call for for software program which can research huge datasets which could scale as much as 10 billion rows. Apache Hadoop has been created to address such heavy computational initiatives. Mahout won attractiveness for supplying facts mining category algorithms that may be used with such form of datasets.
"Apache Mahout Cookbook" offers a clean, scope-oriented method of the Mahout global for either novices in addition to complicated clients. The e-book offers an perception on the way to write diversified facts mining algorithms for use within the Hadoop atmosphere and select the easiest one suiting the duty in hand.
"Apache Mahout Cookbook" appears on the a variety of Mahout algorithms on hand, and offers the reader a clean solution-centered procedure on how one can clear up diversified info mining projects. The recipes begin effortless yet get steadily complex. A step by step technique will advisor the developer within the various initiatives keen on mining a big dataset. additionally, you will easy methods to code your Mahout’s information mining set of rules to figure out the simplest one for a selected job. Coupled with this, an entire bankruptcy is devoted to loading info into Mahout from an exterior RDMS process. loads of cognizance has additionally been wear utilizing your info mining set of rules inside of your code with a purpose to be capable to use it in an Hadoop atmosphere. Theoretical points 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 booklet offers the code for reusing it utilizing Maven in addition to the Maven Mahout resource code.
By the tip of this publication it is possible for you to to code your process to do a number of facts mining projects with various algorithms and to judge and select the easiest ones in your tasks.
What you are going to examine from this book
- Configure from scratch an entire improvement surroundings for Mahout with NetBeans and Maven
- Handle sequencefiles for greater performance
- Query and shop effects into an RDBMS approach with SQOOP
- Use logistic regression to foretell the following step
- Understand textual content mining of uncooked facts 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 assist novices in addition to complicated programmers get conversant in the good points of Mahout.
Who this publication is written for
"Apache Mahout Cookbook" is excellent for builders who are looking to have a clean and quick advent to Mahout coding. No past wisdom of Mahout is needed, or even expert builders or procedure directors will enjoy the quite a few recipes presented.