Knowledge and Data Science Research Centre (KDRC)

A centre of excellence in digital research.

Books

The Knowledge and Data Science Research Centre (KDRC) has produced multiple textbooks and research books with SpringerNature, one of the largest and most prestigious publisher of books. 

Applied Text Mining

  • Covers the concepts, theories, and implementations of text mining and natural language processing
  • Explains core topics like feature engineering, text classification, clustering, summarization, and topic mapping
  • Includes sample implementations for every text mining task based on Python and Spacy and NLTK libraries
  • Buy it directly from Springer: https://link.springer.com/book/9783031519161

Data Science Concepts and Techniques with Applications (Second Edition), 2023

  • Textbook which comprehensively covers both fundamental and advanced topics related to data science
  • Presents data pre-processing, classification, clustering, text and pattern mining, deep learning, regression analysis
  • Includes an introduction to the open source tool WEKA, the Waikato Environment for Knowledge Analysis
  • Buy it directly from Springer: https://link.springer.com/book/10.1007/978-981-15-6133-7

Data Science Concepts and Techniques with Applications, 2020

  • The textbook provides details about the fundamental tools and techniques used for data analysis
  • Covers state-of-the-art techniques for data analytics, future research directions and guidance on data analytics
  • Illustrates concepts with simple and intuitive examples, along with step-wise explanations
  • Includes Python and R programming language tutorials for data science
  • Buy it directly from Springer: https://link.springer.com/book/10.1007/978-981-15-6133-7

Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications (Second Edition), 2019

  • Provides a comprehensive introduction to rough set-based feature selection
  • Enables the reader to systematically study all topics in rough set theory (RST)
  • The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning
  • Also covers the dominance-based rough set approach and fuzzy rough sets
  • Buy it directly from Springer: https://link.springer.com/book/10.1007/978-981-32-9166-9

Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications, 2017

  • Complete introduction of FS and RST (including background and practical applications)
  • In-depth analysis of state-of-the-art tools and techniques (including strong and weak points and complexity analysis of each technique)
  • Working code of RST functionality and state of the art approaches along with explanation and complexity analysis of each
  • Buy it directly from Springer: https://link.springer.com/book/10.1007/978-981-32-9166-9