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2017 | Book

Knowledge Management Read chapter Read first chapter

Descriptive Data Mining

Author: David L. Olson

Publisher: Springer Singapore

Book Series : Computational Risk Management

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

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.

Table of Contents

Frontmatter
Chapter 1. Knowledge Management
Abstract
Knowledge management is an overarching term referring to the ability to identify, store, and retrieve knowledge. Identification requires gathering the information needed 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. Storage and retrieval of data involves database management, using many tools developed by computer science. Thus knowledge management involves understanding what knowledge is important to the organization, understanding systems important to organizational decision making, database management, and analytic tools of data mining.
David L. Olson
Chapter 2. Data Visualization
Abstract
Data and information are important resources to be managed in modern organizations. Business analytics refers to the skills, technologies, applications 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.
David L. Olson
Chapter 3. Market Basket Analysis
Abstract
Knowledge discovery is the effort to find information from data. In contemporary terms, it is the application of tools (from statistics and from artificial intelligence) to extract interesting patterns from data stored in large databases. Here interesting means non-trivial, implicit, previously unknown, and easily understood and described knowledge that can be used (actionable).
David L. Olson
Chapter 4. Recency Frequency and Monetary Model
Abstract
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.
David L. Olson
Chapter 5. Association Rules
Abstract
Association rules seek to identify combinations of things that frequently occur together (affinity analysis). 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.
David L. Olson
Chapter 6. Cluster Analysis
Abstract
This chapter covers a number of aspects of cluster analysis. Initially, it presents clustering manually, using standardized data. This is to show how basic 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.
David L. Olson
Chapter 7. Link Analysis
Abstract
Link analysis considers the relationship between entities in a network. They are interesting in many contexts, to include social network analysis (Knoke and Yang 2008) which has been used to measure social relationships, to include social media and collaboration networks. People (or customers) can be represented as nodes and the relationships between them can be links in a graph. In biological science they have been applied to analyze protein interactions. They also have been applied to law enforcement and terrorism.
David L. Olson
Chapter 8. Descriptive Data Mining
Abstract
This book addresses the basic aspect of data mining, descriptive analytics. As stated in the preface, this concerns studying what has happened, looking at various forms of statistics to gain understanding of the state of whatever field is being examined. The book begins with a chapter on knowledge management, seeking to provide a context of analytics in the overall framework of information management.
David L. Olson
Backmatter
Metadata
Title
Descriptive Data Mining
Author
David L. Olson
Copyright Year
2017
Publisher
Springer Singapore
Electronic ISBN
978-981-10-3340-7
Print ISBN
978-981-10-3339-1
DOI
https://doi.org/10.1007/978-981-10-3340-7

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