The future of data mining / Cem Ufuk Baytar, PhD, editor.

Format:

Book

Contributor:

Baytar, Ufuk, editor.

Series:

Research methodology and data analysis.

Research Methodology and Data Analysis

Language:

English

Subjects (All):

Data mining.

Physical Description:

1 online resource (156 pages)

Edition:

1st ed.

Place of Publication:

New York : Nova Science Publishers, [2022]

Summary:

"The purpose of this book is to discuss data mining, which is a subset of data science, from a variety of perspectives. With the technological advances of recent years, new software and hardware-based systems are available in most business environments. With these systems, data production continues to increase in personal, corporate, commercial and many other areas. Information systems convert raw data, which alone are not so meaningful, into information after the processes are applied. Database systems are necessary for the storage and management of the information generated. Revealing meaningful relationships hidden in a stack of high-volume data shows the function of data mining. Processing big data has become important to produce information that will support business decisions and be a strategic tool in today's competitive environment. In this context, the effectiveness of data mining applications is increasing day by day as a decision support system to develop marketing strategies in every sector by identifying customer behavior and target groups"-- Provided by publisher.

Contents:

Intro

Contents

Preface

Acknowledgments

Chapter 1

Data Analytics Applied to the Human Resources Industry

Abstract

Introduction

Data Analytics

Human Resources Analytics

Conclusion

References

Chapter 2

Toxicogenomics Data Mining as a Promising Prioritization Tool in Toxicity Testing

Useful Databases and Tools for Data Mining in Toxicology

Data Mining Examples

Advantages

Limitations

Chapter 3

Applications of Data Mining Algorithms for Customer Recommendations in Retail Marketing

Literature Review

Methodology

Findings and Results

Chapter 4

Analysis of Customer Churn in Banking Industry Using Data Mining

Digitalization and Online Banking

Customer Relations Management and Data Analysis

Customer Loyalty and Data Analysis

Tools and Methodology

Understanding the Data

Data Preparation

Data Modeling

Decision Tree Algorithm

The Random Forest Algorithm

Artificial Neural Networks

Chapter 5

The Crowdsourcing Concept-Based Data Mining Approach Applied in Prosumer Microgrids

Crowdsourcing Energy System

The Problem Formulation

The Prosumer Profiling Using Data Mining Method

Optimal Allocation of Prosumers in Local Microgrids

Results and Discussion

Chapter 6

Active Learning

Query Strategies

Use Case

Industry and Robotics

Healthcare

Cybersecurity

Chapter 7

Prediction of General Anxiety Disorder Using Machine Learning Techniques

Background

Materials and Methods

SVM.

Decision Tree

ANN

RF

KNN

Performance Metrics

Data Description

Normalization Filter

Experiments and Results

ANN Results

K-Nearest Neighbor Results

Decision Tree Results

Random Forest Results

Comparison of Performance Metrics for ANN, KNN, DT, RF, SVM

Editor's Contact Information

Index

Blank Page

Blank Page.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Other Format:

Print version: Baytar, Ufuk The Future of Data Mining

ISBN:

9798886973150