Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021

This book discusses integration techniques for data mining, presenting high-quality research papers from the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021). It addresses the challenges of integrating computational intelligence and data mining, disseminating innovative research directions and current issues in the field.

Format: Hardback
Length: 764 pages
Publication date: 07 May 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into a wide range of methods and techniques aimed at enhancing the overall objective of data mining. Presented at the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021), held at Aditya Institute of Technology and Management in Tekkali, Andhra Pradesh, India, from December 11 to 12, 2021, the book showcases a collection of high-quality peer-reviewed research papers. Its primary focus lies in addressing the complexities and challenges associated with the seamless integration of two fundamental disciplines of computer science: computational intelligence and data mining. By disseminating knowledge about innovative and active research directions in these fields, as well as discussing current issues and applications related to them, the book serves as a valuable resource for scholars and practitioners alike.

The book explores various approaches and strategies for integrating computational intelligence and data mining, aiming to unlock their full potential and facilitate effective data analysis and knowledge discovery. It discusses the challenges and opportunities arising from the fusion of these two domains, including the development of new algorithms, models, and methodologies that can handle large-scale data sets, extract meaningful insights, and make informed decisions.

One of the key themes of the book is the exploration of machine learning and deep learning techniques, which have gained significant attention in recent years for their ability to analyze and interpret complex data. The book discusses the application of these techniques in data mining, such as classification, regression, clustering, and anomaly detection, highlighting their advantages and limitations. It also explores the integration of machine learning with other computational intelligence techniques, such as natural language processing, decision trees, and genetic algorithms, to enhance the performance and accuracy of data mining systems.

Another important aspect of the book is the discussion of current issues and applications in data mining. It addresses topics such as big data analytics, data privacy and security, data visualization, and decision support systems. The book provides insights into the latest research and developments in these areas, highlighting the challenges and opportunities faced by researchers and practitioners in the field.

Furthermore, the book emphasizes the importance of interdisciplinary collaboration in data mining. It discusses the role of domain experts, researchers, and practitioners in integrating computational intelligence and data mining to address real-world problems and achieve societal benefits. It highlights the need for interdisciplinary research, education, and training programs to promote the growth and development of the field.

In conclusion, this comprehensive book offers a valuable resource for scholars, researchers, and practitioners interested in advancing the field of data mining. It provides a comprehensive overview of integration methods and techniques, discusses cutting-edge research directions, and addresses current issues and applications in data mining. By fostering interdisciplinary collaboration and promoting the exchange of knowledge, the book contributes to the growth and development of the field.

Weight: 1346g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811694462
Edition number: 1st ed. 2022