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Titlebook: Descriptive Data Mining; David L. Olson Book 20171st edition Springer Nature Singapore Pte Ltd. 2017 Business Analytics.Data Mining.Descri

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書(shū)目名稱(chēng)Descriptive Data Mining
編輯David L. Olson
視頻videohttp://file.papertrans.cn/269/268302/268302.mp4
概述Includes a discussion of descriptive analytic modeling.Covers visualization in the context of data mining.Demonstrates modeling with R and other open source software products.Includes supplementary ma
叢書(shū)名稱(chēng)Computational Risk Management
圖書(shū)封面Titlebook: Descriptive Data Mining; David L. Olson Book 20171st edition Springer Nature Singapore Pte Ltd. 2017 Business Analytics.Data Mining.Descri
描述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 w
出版日期Book 20171st edition
關(guān)鍵詞Business Analytics; Data Mining; Descriptive Data Mining; Visualization; Open Source Software; Cluster An
版次1
doihttps://doi.org/10.1007/978-981-10-3340-7
isbn_softcover978-981-10-9847-5
isbn_ebook978-981-10-3340-7Series ISSN 2191-1436 Series E-ISSN 2191-1444
issn_series 2191-1436
copyrightSpringer Nature Singapore Pte Ltd. 2017
The information of publication is updating

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Nadjma Yassari,Lena-Maria M?ller?Recency, Frequency, and Monetary (RFM)analysis seeks to identify customers who are more likely to respond to new offers. While lift looks at the static measure of response to a particular campaign, RFM keeps track of customer transactions by time, by frequency, and by amount.
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Association Rules,Association rules seek to identify combinations of things that frequently occur together (.). This is also the basis of market basket analysis, which we discussed in terms of correlation and Jaccard ratios. Association rules take things a step further by applying a form of machine learning, the most common of which is the apriori algorithm.
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Function of Filaggrin and Its Metabolitesneeded and to analyze available data to make effective decisions regarding whatever the organization does. This include research, digging through records, or gathering data from wherever it can be found. . and . of data involves database management, using many tools developed by computer science. Th
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File Sharing in Peer-to-Peer-Netzwerkens and practices for exploration and investigation of past business performance to gain insight and aid business planning. The focus is on developing new insights and understanding based on data and statistical analysis. The emphasis is on fact-based management to drive decision making.
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Ayesha Shahid,Isfandyar Ali Khanbasic algorithms work. The second section shows how software works on this standardized data. The third section will demonstrate software with original data not requiring standardization. If you don’t care what computers are doing, you can proceed to this section.
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