Data Classification and Incremental Clustering in Data Mining and Machine Learning
Chakraborty, Sanjay.
Data Classification and Incremental Clustering in Data Mining and Machine Learning [electronic resource] / by Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta. - 1st ed. 2022. - 1 online resource - EAI/Springer Innovations in Communication and Computing, 2522-8609 . - EAI/Springer Innovations in Communication and Computing, .
Introduction to Data Mining & Knowledge Discovery -- A Brief Concept on Machine Learning -- Supervised Learning based Data Classification and Incremental Clustering -- Data Classification and Incremental Clustering using Unsupervised Learning -- Research Intention towards Incremental Clustering -- Applications and Trends in Data Mining & Machine Learning -- Feature subset selection techniques with Machine Learning -- Data Mining Based variant subsets features.
This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques. Provides a comprehensive review of various data mining techniques and architecture, primarily focusing on supervised and unsupervised learning Presents hands-on coding examples using three popular coding platforms: R, Python, and Java Includes case-studies, examples, practice problems, questions, and solutions for students and professionals, focusing on machine learning and data science.
9783030930882
10.1007/978-3-030-93088-2 doi
Telecommunication.
Computational intelligence.
Computer vision.
Data mining.
Communications Engineering, Networks.
Computational Intelligence.
Computer Vision.
Data Mining and Knowledge Discovery.
Machine learning
QA76.9.D343
Data Classification and Incremental Clustering in Data Mining and Machine Learning [electronic resource] / by Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta. - 1st ed. 2022. - 1 online resource - EAI/Springer Innovations in Communication and Computing, 2522-8609 . - EAI/Springer Innovations in Communication and Computing, .
Introduction to Data Mining & Knowledge Discovery -- A Brief Concept on Machine Learning -- Supervised Learning based Data Classification and Incremental Clustering -- Data Classification and Incremental Clustering using Unsupervised Learning -- Research Intention towards Incremental Clustering -- Applications and Trends in Data Mining & Machine Learning -- Feature subset selection techniques with Machine Learning -- Data Mining Based variant subsets features.
This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques. Provides a comprehensive review of various data mining techniques and architecture, primarily focusing on supervised and unsupervised learning Presents hands-on coding examples using three popular coding platforms: R, Python, and Java Includes case-studies, examples, practice problems, questions, and solutions for students and professionals, focusing on machine learning and data science.
9783030930882
10.1007/978-3-030-93088-2 doi
Telecommunication.
Computational intelligence.
Computer vision.
Data mining.
Communications Engineering, Networks.
Computational Intelligence.
Computer Vision.
Data Mining and Knowledge Discovery.
Machine learning
QA76.9.D343
