Data Mining: Processes and Applications
ISBN: 9781839379383
Year: 2023
Binding: HB
Price: US $ 250.00
About Book
"Data mining is a process of searching for, extracting, and analysing (which may include) various types of text graphic patterns (as calligraphic, for example), language and literary figures, stylistics, in large amounts of textual or mixed visual and textual data sets. It also involves methods at the intersection of machine learning, formal linguistics analyses such as textual statistics, and database systems. Data mining has evolved from a purely computer linguistics field to an interdisciplinary subfield of linguistics, art sciences, and computer science, as well as linguistic and mathematical statistics, with the overarching goal of extracting information (via analyses, computer programmes, and intelligent methods) from a given data set, formulating analysis results, and further transforming this extracted information into a summarised and comprehensible structure. Its fundamental components include machine learning and statistical analysis, as well as data management chores performed prior to analysis. The introduction of machine learning algorithms and artificial intelligence (AI) technologies has simplified the process of mining enormous data sets like as customer databases, transaction records, and log files from web servers, mobile apps, and sensors.
This book aims to shed light on some of the unexplored aspects of data mining. It picks up individual topics and explains their need and contribution in the context of the growth of this subject. It explores the principal techniques of data mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. It deals with visual data mining, data mining perspective, data mining techniques in pharmacovigilance and load forecasting using data mining. It also sheds light on improving decision support systems, environmental data mining, classification of crew productivity, enhanced pass data mining mechanism and identification of nonlinear systems. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. Selected concepts that redefine this field have been presented in the book. This book is intended for computer Science students, application developers, business professionals, and researchers who seek information on data mining.
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