Mahmoud Parsian 
Data Algorithms 
Recipes for Scaling Up with Hadoop and Spark

If you are ready to dive into the Map Reduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed Map Reduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. Youll learn how to implement the appropriate Map Reduce solution with code that you can use in your projects.Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of Map Reduce, Hadoop, and Spark.Topics include:Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes)Using huge genomic data to sequence DNA and RNANaive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNASocial network analysis (recommendation systems, counting triangles, sentiment analysis)