TY  - BOOK
AU  - KULKARNI, PARAG.
AU  - BROWN, META S.
AU  - JOSHI, SARANG.
TI  - Big Data Analytics
SN  - 9788120351165
U1  - 006.312 23
PY  - 2016///
CY  - delhi
PB  - phi learning
KW  - data mining
KW  - big data
KW  - textbook
N1  - Preface
1. Introduction
2. Data Mining and Modelling
3. Big Data Mining—Application Perspective
4. Long Live the King of Big Data: The Context
5. Big Data Text Categorization and Topic Modelling
6. Multi-label Big Data Mining
7. Distributed High Dimensional Data Clustering for Big Data
8. Machine Learning and Incremental Learning with Big Data
9. Analytics in Today’s Business World
10. Conclusion
Annexure I: Introduction to Hadoop—A Big Data Perspective
Annexure II: Installing and Running GATE
Bibliography • Index
N2  - The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

The book is designed for the senior level undergraduate, and postgraduate students of computer science and engineering.

KEY FEATURES
• Contains numerous examples and case studies.
• Discusses Apache’s Hadoop—a software framework that enables distributed processing of large datasets across the clusters of computing machines.
• Incorporates review questions, MCQs, laboratory assignments and critical thinking questions at the end of the chapters, wherever required
ER  -