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Data algorithms

by Mahmoud Parsian | 28 July 2015
Category: Databases
If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You'll learn how to implement the appropriate MapReduce 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 MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactionsData 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 predictionRecommendation algorithms and pairwise document similarityLinear regression, Cox regression, and Pearson correlationAllelic frequency and mining DNASocial network analysis (recommendation systems, counting triangles, sentiment analysis)
€78.39
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If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You'll learn how to implement the appropriate MapReduce 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 MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactionsData 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 predictionRecommendation algorithms and pairwise document similarityLinear regression, Cox regression, and Pearson correlationAllelic frequency and mining DNASocial network analysis (recommendation systems, counting triangles, sentiment analysis)
Quantity:
In stock online
Delivery 5-7 Days
Eligible for free delivery
235 Reward Points

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

€78.39
In stock online
Delivery 5-7 Days
Eligible for free delivery
Quantity:
235 Reward Points

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

Product Description

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You'll learn how to implement the appropriate MapReduce 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 MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactionsData 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 predictionRecommendation algorithms and pairwise document similarityLinear regression, Cox regression, and Pearson correlationAllelic frequency and mining DNASocial network analysis (recommendation systems, counting triangles, sentiment analysis)

Product Details

Data algorithms

ISBN9781491906187

Format

Publisher (28 July. 2015)

No. of Pages778

Weight1294

Language English (United States)

Dimensions 233 x 178 x 39