03647nam a22005175i 4500 978-981-10-3340-7 DE-He213 20210619020829.0 cr nn 008mamaa 161210s2017 si | s |||| 0|eng d 9789811033407 978-981-10-3340-7 10.1007/978-981-10-3340-7 doi HF5548.125-5548.6 KJQ bicssc BUS070030 bisacsh KJQ thema 658.4038 23 Olson, David L. author. aut http://id.loc.gov/vocabulary/relators/aut Descriptive Data Mining [electronic resource] / by David L. Olson. 1st ed. 2017. Singapore : Springer Singapore : Imprint: Springer, 2017. XI, 116 p. 63 illus., 60 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Risk Management, 2191-1436 Chapter 1 Knowledge Management -- Chapter 2: Data Visualization -- Chapter 3 Market Basket Analysis -- Chapter 4 Recency Frequency and Monetary Model -- Chapter 5 Association Rules -- Chapter 6 Cluster Analysis -- Chapter 7 Link Analysis -- Chapter 7 Link Analysis -- Chapter 8 Descriptive Data Mining -- References -- Index. This book offers an overview of knowledge management. It starts with an introduction to the subject, placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis. Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Both R and Rattle are free to students. Chapter 3 then describes market basket analysis, comparing it with more advanced models, and addresses the concept of lift. Subsequently, Chapter 4 describes smarketing RFM models and compares it with more advanced predictive models. Next, Chapter 5 describes association rules, including the APriori algorithm and provides software support from R. Chapter 6 covers cluster analysis, including software support from R (Rattle), KNIME, and WEKA, all of which are open source. Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output. Chapter 8 concludes the monograph. Using business-related data to demonstrate models, this descriptive book explains how methods work with some citations, but without detailed references. The data sets and software selected are widely available and can easily be accessed. Big data. Data mining. Risk management. Big Data/Analytics. https://scigraph.springernature.com/ontologies/product-market-codes/522070 Data Mining and Knowledge Discovery. https://scigraph.springernature.com/ontologies/product-market-codes/I18030 Risk Management. https://scigraph.springernature.com/ontologies/product-market-codes/612040 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789811033391 Printed edition: 9789811033414 Printed edition: 9789811098475 Computational Risk Management, 2191-1436 https://doi.org/10.1007/978-981-10-3340-7 ZDB-2-BUM ZDB-2-SXBM Business and Management (SpringerNature-41169) Business and Management (R0) (SpringerNature-43719)