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03647nam a22005175i 4500 |
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20210619020829.0 |
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161210s2017 si | s |||| 0|eng d |
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|a 9789811033407
|9 978-981-10-3340-7
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|a 10.1007/978-981-10-3340-7
|2 doi
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|a HF5548.125-5548.6
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|a 658.4038
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|a Olson, David L.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Descriptive Data Mining
|h [electronic resource] /
|c by David L. Olson.
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| 250 |
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|a 1st ed. 2017.
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| 264 |
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|a Singapore :
|b Springer Singapore :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XI, 116 p. 63 illus., 60 illus. in color.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Computational Risk Management,
|x 2191-1436
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| 505 |
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|a 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.
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|a 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.
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| 650 |
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|a Big data.
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| 650 |
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|a Data mining.
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| 650 |
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|a Risk management.
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| 650 |
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|a Big Data/Analytics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/522070
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| 650 |
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|a Data Mining and Knowledge Discovery.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030
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| 650 |
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|a Risk Management.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/612040
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9789811033391
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| 776 |
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|i Printed edition:
|z 9789811033414
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| 776 |
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|i Printed edition:
|z 9789811098475
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| 830 |
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|a Computational Risk Management,
|x 2191-1436
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| 856 |
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|u https://doi.org/10.1007/978-981-10-3340-7
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| 912 |
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|a ZDB-2-BUM
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| 912 |
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|a ZDB-2-SXBM
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| 950 |
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|a Business and Management (SpringerNature-41169)
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| 950 |
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|a Business and Management (R0) (SpringerNature-43719)
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