Getting from Data to Knowledge: An Introduction to Data Mining

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CIDRIS Seminar on Data Mining

February 24, 2006


Getting from Data to Knowledge:

An Introduction to Data Mining


By Daniel T. Larose, Ph.D.

Director, Data Mining @CCSU

Associate Professor of Statistics,

Central Connecticut State University

www.math.ccsu.edu/larose/



Abstract


According to the Gartner Group, “
Data mining is the process of discovering meaningful new correlations,
patterns and trends by sifting through large amounts of data stored in repos
itories, using pattern recognition
technologies as well as statistical and mathematical techniques
.” As early as 1984, in his book
Megatrends
,
John Naisbitt observed that “We are drowning in information but starved for knowledge.” The problem
today is no
t that there is not enough data and information streaming in. We are in fact inundated with data
in most fields. Rather, the problem is that there are not enough trained
human
analysts available who are
skilled at translating all of this data into knowle
dge, and thence up the taxonomy tree into wisdom.


In this introductory seminar, we examine how the process of data mining fits into
the overall business or research picture, using the CRISP
-
DM cross
-
industry
standard process for data mining. We examine
some of the tasks of data mining,
including prediction, clustering, classification, and market basket analysis. The
talk uses material from Dr. Larose’s books,
Discovering Knowledge in Data: An
Introduction to Data Mining

(Wiley, 2005) and
Data Mining Met
hods and Models

(Wiley, 2006). The prerequisite is a love for finding patterns and trends in data, or
a need for the same.




About the Speaker


Professor Larose, Director of Data Mining @CCSU, received his Ph.D. in
Statistics from the University of Conn
ecticut in Storrs in 1996 (
Bayesian
Approaches to Meta
-
Analysis
; Advisor: Dr. Dipak K. Dey).


He is the author of a
data mining book series from Wiley Interscience:
Discovering Knowledge from
Data: An Introduction to Data Mining

was published in January 2
005. The second
book,
Data Mining Methods and Models
, appeared in January 2006. The third
book,
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and
Usage

(with Dr. Zdravko Markov) will appear in January 2007. He co
-
authored
Ready Set

Run! A Student Guide to SAS Software for Microsoft Windows
, and is
currently writing
Discovering Statistics
, an introductory statistics textbook.
His
consulting work includes a $750,000 Phase II grant from the Air Force Office of
Research,
Storage Effi
cient Data Mining of High Speed Data Streams
. He
designed, developed, and directs the world’s first online Master of Science program in data mining.