- Algorithms for clustering data, Anil K. Jain, Richard C. Dubes
- Feature Weighting for Clustering, Renato Cordeiro De Amorim
- KNN Classifier and K-Means Clustering for Robust Classification of Epilepsy, by Harikumar Rajaguru, Sunil Kumar Prabhakar
- Partitional Clustering Algorithms, edited by M. Emre Celebi
- Clustering, Rui Xu, Don Wunsch
- Mathematical Classification and Clustering, by Boris Mirkin
Hands-On Big Data Modeling
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Hands-On Big Data Modeling
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Overview of this book
Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements.
To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets.
By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently.
Table of Contents (17 chapters)
Preface
Free Chapter
Introduction to Big Data and Data Management
Data Modeling and Management Platforms
Defining Data Models
Categorizing Data Models
Structures of Data Models
Modeling Structured Data
Modeling with Unstructured Data
Modeling with Streaming Data
Streaming Sensor Data
Concept and Approaches of Big-Data Management
DBMS to BDMS
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