Olson, David L. creator author. aut http://id.loc.gov/vocabulary/relators/aut Wu, Desheng Dash. author. aut http://id.loc.gov/vocabulary/relators/aut Luo, Cuicui. author. aut http://id.loc.gov/vocabulary/relators/aut Nabavi, Majid. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text si 2024 1st ed. 2024. monographic eng

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access X, 196 p. 85 illus., 78 illus. in color. online resource. This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence. Data Mining in Business -- Data Mining Processes -- Data Mining Software -- Association Rules -- Cluster Analysis.-Regression Algorithms in Data Mining -- Classification Tools -- Variable Selection -- Dataset Balancing. by David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi. Business information services Business Information Systems HF54.5-.56 9789819747726 https://doi.org/10.1007/978-981-97-4772-6 https://doi.org/10.1007/978-981-97-4772-6 240730 20251030114925.0 978-981-97-4772-6