Practical Data Mining Techniques and Applications

Regular price €65.99
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AI
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B01=Ketan Shah
B01=Neepa Shah
B01=Neeraj Parolia
B01=Vinaya Sawant
Candidate Itemsets
Canny Edge Detection Algorithm
Category1=Non-Fiction
Category=UNF
COP=United Kingdom
Data Analysis Unit
data analytics
Data Mining Algorithms
data science
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Diabetes Prediction
Distributed Data Mining
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Execution Time
Flood Fill
Flood Fill Algorithm
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Gray Scaling
Interactive Genetic Algorithm
KNN Model
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LDA
LDA Algorithm
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machine learning
MLP Model
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Pre-processed Datasets
Price_€50 to €100
PS=Forthcoming
Raspberry Pi
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Support Vector Data Description
Transaction Scans
Unsupervised Machine Learning

Product details

  • ISBN 9781032486772
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 29 Nov 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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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.