A hands-on guide to making valuable decisions from data using
advanced data mining methods and techniques
This second installment in the Making Sense of Data
series continues to explore a diverse range of commonly used
approaches to making and communicating decisions from data. Delving
into more technical topics, this book equips readers with advanced
data mining methods that are needed to successfully translate raw
data into smart decisions across various fields of research
including business, engineering, finance, and the social
sciences.
Following a comprehensive introduction that details how to
define a problem, perform an analysis, and deploy the results,
Making Sense of Data II addresses the following key
techniques for advanced data analysis:
* Data Visualization reviews principles and methods for
understanding and communicating data through the use of
visualization including single variables, the relationship between
two or more variables, groupings in data, and dynamic approaches to
interacting with data through graphical user interfaces.
* Clustering outlines common approaches to clustering data
sets and provides detailed explanations of methods for determining
the distance between observations and procedures for clustering
observations. Agglomerative hierarchical clustering,
partitioned-based clustering, and fuzzy clustering are also
discussed.
* Predictive Analytics presents a discussion on how to
build and assess models, along with a series of predictive
analytics that can be used in a variety of situations including
principal component analysis, multiple linear regression,
discriminate analysis, logistic regression, and Naïve
Bayes.
* Applications demonstrates the current uses of data mining
across a wide range of industries and features case studies that
illustrate the related applications in real-world scenarios.
Each method is discussed within the context of a data mining
process including defining the problem and deploying the results,
and readers are provided with guidance on when and how each method
should be used. The related Web site for the series
(www.makingsenseofdata.com) provides a hands-on data analysis and
data mining experience. Readers wishing to gain more practical
experience will benefit from the tutorial section of the book in
conjunction with the
Traceis¯TM software, which is freely
available online.
With its comprehensive collection of advanced data mining
methods coupled with tutorials for applications in a range of
fields, Making Sense of Data II is an indispensable book for
courses on data analysis and data mining at the upper-undergraduate
and graduate levels. It also serves as a valuable reference for
researchers and professionals who are interested in learning how to
accomplish effective decision making from data and understanding if
data analysis and data mining methods could help their
organization.
Sobre o autor
Glenn J. Myatt, Ph D, is cofounder of Leadscope, Inc. and a Partner of Myatt & Johnson, Inc., a consulting company that focuses on business intelligence application development delivered through the Internet. Dr. Myatt is the author of Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining, also published by Wiley. WAYNE P. JOHNSON, MSc., is cofounder of Leadscope, Inc. and a Partner of Myatt & Johnson, Inc. Mr. Johnson has over two decades of experience in the design and development of large software systems, and his current professional interests include human-computer interaction, information visualization, and methodologies for contextual inquiry.
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