Название: Practical Data Mining Techniques and Applications
Автор: Ketan Shah, Neepa Shah, Vinaya Sawant
Издательство: CRC Press
Год: 2023
Страниц: 215
Язык: английский
Формат: pdf (true)
Размер: 12.5 MB

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.

The data is everywhere now, growing rapidly at an express rate. We are deluged by data – scientific data, medical data, demographic data, financial data, and marketing data as storage is inexpensive and getting even less so, as are data sensors. This is simply because collection and storage of data is easier than ever before. Every enterprise benefits from collecting and analysing its data. For instance, hospitals can use this analysis in spotting trends and anomalies in patient records, and search engines can do perform better page ranking and advertisement placement. Besides these, intrusion detection in cybersecurity and computer networks; keeping track of the energy consumption of household appliances; pattern analysis in bioinformatics and pharmaceutical data; financial and business intelligence data; and spotting trends in blogs, Twitter, and other social networking sites are few more examples. Data mining is often defined as finding hidden and useful information in a database. It is a process of extracting and discovering patterns in large datasets involving methods at the intersection of Machine Learning, statistics, and database systems.

After gathering the data, the LDA analysis was used. In order to conduct the LDA analysis, we used a combination of Gensim library and Natural Language Toolkit (NLTK) library. Initially, duplicate tweets were identified and removed using a custom code in Python. Since Machine Learning models cannot comprehend raw text, there are several steps taken to convert tweets into numeric data that can be used to train the model.

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.






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