Handbook of Statistical Analysis and Data Mining Applications - 1st EditionThe Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers both academic and industrial through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Business analysts, scientists, engineers, researchers, and students in statistics and data mining.
Handbook of Statistical Analysis and Data Mining Applications
You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views. This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics.
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This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology. Unable to display preview. Download preview PDF. Skip to main content.