Knowledge Discovery Using Big Data Analytics - Agarwal, Ruchi;Vats, Satvik;Singh, Sunny
23,99 €
inkl. MwSt.

Versandfertig in 6-10 Tagen
payback
12 °P sammeln
  • Broschiertes Buch

In the present scenario, Hadoop is just like the kernel for big data, having distributed storage and compute capabilities to handle structured/semi-structured/unstructured data. Hadoop framework is also utilized for data warehousing and in the field of data science, which makes new informative discoveries about data. In this book two advanced algorithm named as K-Means Clustering and Frequent Item sets mining are applied on Hadoop MapReduce environment, which presents them in a problem/solution format. Predictive analysis of the output is done with the help of Tableau. Each problem is explored…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
In the present scenario, Hadoop is just like the kernel for big data, having distributed storage and compute capabilities to handle structured/semi-structured/unstructured data. Hadoop framework is also utilized for data warehousing and in the field of data science, which makes new informative discoveries about data. In this book two advanced algorithm named as K-Means Clustering and Frequent Item sets mining are applied on Hadoop MapReduce environment, which presents them in a problem/solution format. Predictive analysis of the output is done with the help of Tableau. Each problem is explored step by step, which automatically helps the reader in growing more comfortable with Hadoop in the world of big data. This hand book helps the reader to demonstrate how the real world data is handled using hadoop framework. It also helps readers in understanding the basic concepts of MapReduce and Hadoop Distributed File System (HDFS).
Autorenporträt
The Authors of this book have been actively involved in the research field of Big Data Analytics. They have published various books and papers in international refereed journals and prestigious conferences. Presently, they are working on "Hybrid Approach of Frequent Item-sets Mining and K-Means Clustering" with integration of encryption algorithm.