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Practical Data Mining Techniques and Applications

€198.40
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advanced data mining applications
AI
Candidate Itemsets
Canny Edge Detection Algorithm
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computational intelligence
Data Analysis Unit
data analytics
Data Mining Algorithms
data science
Diabetes Prediction
distributed computing
Distributed Data Mining
Document Clustering
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Execution Time
federated learning
Flood Fill
Flood Fill Algorithm
Frequent Itemsets
Gray Scaling
health informatics
Interactive Genetic Algorithm
KNN Model
LDA
LDA Algorithm
LDA Model
LDA Topic
machine learning
MLP Model
NB Classifier
Pre-processed Datasets
Raspberry Pi
semantic clustering
social media analysis
Support Vector Data Description
Transaction Scans
Unsupervised Machine Learning

Product details

  • ISBN 9781032232676
  • Weight: 520g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Jun 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

Data mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users’ goals and mold the algorithms with respect to users’ perspectives.

Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied data science techniques.

By highlighting an overall picture of the field, introducing various mining techniques, and focusing on different applications and research directions using these methods, this book can motivate discussions among academics, researchers, professionals, and students to exchange and develop their views regarding the dynamic field that is data mining.