Olson David L. Descriptive Data Mining

Springer, 2019. — 130 p. — (Computational Risk Management). — ISBN: 98113718062nd.edThis book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis.Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.Knowledge Management
Computer Support Systems
Examples of Knowledge Management
Data Mining Descriptive ApplicationsData Visualization
Data Visualization
R Software
Energy Data
Basic Visualization of Time SeriesMarket Basket Analysis
Definitions
Co-occurrence
Demonstration
Market Basket LimitationsRecency Frequency and Monetary Analysis
Dataset 1
Balancing Cell
Lift
Value Function
Data Mining Classification Models
Dataset 2Association Rules
Methodology
The Apriori Algorithm
Association Rules from Software
Non-negative Matric FactorizationCluster Analysis
K-Means Clustering
Clustering Methods Used in Software
SoftwareLink Analysis
Link Analysis Terms
Basic Network Graphics with NodeXL
Network Analysis of Facebook Network or Other Networks
Link Analysis of Your Emails
Link Analysis Application with PolyAnalyst (Olson and Shi 2007)Descriptive Data Mining