1st Edition
Practical Data Mining Techniques and Applications
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.
Preface
Acknowledgments
Contributors
1 Introduction to Data Mining
NEEPA SHAH AND KETAN SHAH
2 Review of Latent Dirichlet Allocation to Understand Motivations to Share Conspiracy Theory: A Case Study of "Plandemic" during COVID 19
SUSHMA KUMBLE, PRATITI DIDDI, AND JEFF CONLIN
3 Near Human-Level Style Transfer
RAHUL PEREIRA, BERYL COUTINHO, JENSLEE DSOUZA, CYRUS FERREIRA, AND VAISHALI JADHAV
4 Semantics-Based Distributed Document Clustering
NEEPA SHAH, SUNITA MAHAJAN, AND KETAN SHAH
5 Application of Machine Learning in Disease Prediction
SANNIDHI RAO, SHREYA KULKARNI, SHIKHA MEHTA, AND NEHA KATRE
6 Federated Machine Learning-Based Bank Customer Churn Prediction
CHIRAG JAGAD, CHIRAG JAIN, DHRUMIL THAKORE, OM NAIK, AND VINAYA SAWANT
7 Challenges and Avenues in the Sophisticated Health-Care System
RAJESH SHARDANAND PRASAD, JAYASHREE RAJESH PRASAD, AND NIHAR M RANJAN
8 Unusual Social Media Behavior Detection Using Distributed Data Stream Mining
AAKASH SANGANI, PRINCY DOSHI, AND VINAYA SAWANT
9 Market Basket Analysis Using Distributed Algorithm
VINAYA SAWANT, KETAN SHAH, AND NEERAJ PAROLIA
10 Identification of Crime Prone-Areas Using Data Mining Techniques
JAINAM RAMBHIA, BHOOMIKA VALANI, SHIVAM VORA, CHAITANYA KUMBAR, AND NEHA KATRE
11 Smart Baby Cradle for Infant Soothing and Monitoring
SAHIL LUNAWAT, ADITYA ADHDUK, VINAYA SAWANT, AND RITIK SANGHVI
12 Word-Level Devanagari Text Recognition
RUTWIK SHAILESH SHAH, HARSHIL SURESH BHORAWAT, HRITIK GANESH SAWANT, AND VINAYA SAWANT
13 Wall Paint Visualizer Using Panoptic Segmentation
MARTIN DEVASIA, SAKSHI SHETTY, SHELDON MOONJELIL, LEANDER PEREIRA, AND VAISHALI JADHAV
14 Fashion Intelligence: An Artificial Intelligence-Based Clothing Fashion Stylist
PAVAN RAVAL, RAJ SHAH, VRUTIK ADANI, LISSA RODRIGUES, AND VAISHALI JADHAV
Index
Biography
Ketan Shah, Neeraj Parolia
