189
Chapter 10
Data Mining for
Organizations: Challenges
andOpportunities
forSmall Developing
States
Corlane Barclay
Contents
Introduction ...................................................................................................... 190
DM and KDDM ..............................................................................................192
DM Tasks .....................................................................................................192
Predictive Methods .......................................................................................192
Descriptive Methods ..................................................................................... 194
KDDM Process ............................................................................................194
KDD Process ...........................................................................................195
CRISP-DM Process .................................................................................196
DM Software Tools/Applications .......................................................................197
RapidMiner ..................................................................................................199
KNIME ........................................................................................................202
R Data Mining .............................................................................................202
190Corlane Barclay
Abstract: Knowledge discovery through data mining facilitates deeper insights into
understanding an organizations operations and environment. An analysis of the litera-
ture revealed the limited use and application of knowledge discovery or data mining
techniques in small economies despite the advanced development and reporting of
this technology in other economies. is may be explained in part by limitation of
resources and a general lack of awareness of the benets of knowledge discovery initia-
tives to the business and government sectors. It is suggested that greater investments
in data mining in key sectors such as sports, agriculture and sheries, manufacturing,
mining, and government administration can yield positive economic benets for small
developing states, including Jamaica. is chapter provides a synthesis of the literature
of the application of data mining across multiple domains such as medicine, insur-
ance, banking and nance, and education, and shares some of the top open-source
data mining tools (e.g., RapidMiner, Waikato Environment for Knowledge Analysis
[Weka], KNIME, and R). is is motivated by the need to improve awareness of the
opportunities that exists from investing in data mining projects and identify some of
challenges that if not properly managed can derail knowledge discovery eorts.
Keywords: Knowledge discovery, data mining, knowledge discovery and data
mining, open-source applications, developing states
Introduction
With the continued growth of structured and unstructured data knowledge discov-
ery and data mining (KDDM) provides even more exciting opportunities for orga-
nizations to improve their operational eciencies and better serve the needs of their
internal and external customers. According to industry statistics, the volume of
Weka ............................................................................................................202
Orange .........................................................................................................202
Application of KDDM ......................................................................................203
Medicine and Health ....................................................................................203
Banking and Financial Services .................................................................... 204
Customer Relationship Management ............................................................205
Government/Public Administration ..............................................................205
Agriculture ................................................................................................... 206
Education .................................................................................................... 206
Manufacturing ..............................................................................................207
Telecommunications .................................................................................... 208
Sports Management ................................................................................ 208
Discussion ........................................................................................................ 208
Concluding Remarks .........................................................................................210
References .........................................................................................................210

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