Graphics Reference
In-Depth Information
Chapter 1
Introduction
Abstract
The main background addressed in this topic should be presented
regarding Data Mining and Knowledge Discovery. Major concepts used through-
out the contents of the rest of the topic will be introduced, such as learning models,
strategies and paradigms, etc. Thus, the whole process known as Knowledge Dis-
covery in Data is provided in Sect.
1.1
. A review on the main models of Data Mining
is given in Sect.
1.2
, accompanied a clear differentiation between Supervised and
Unsupervised learning (Sects.
1.3
and
1.4
, respectively). In Sect.
1.5
, apart from the
two classical data mining tasks, we mention other related problems that assume
more complexity or hybridizations with respect to the classical learning paradigms.
Finally, we establish the relationship between Data Preprocessing with Data Mining
in Sect.
1.6
.
1.1 Data Mining and Knowledge Discovery
Vast amounts of data are around us in our world, raw data that is mainly intractable
for human or manual applications. So, the analysis of such data is now a necessity.
The World Wide Web (WWW), business related services, society, applications and
networks for science or engineering, among others, are continuously generating data
in exponential growth since the development of powerful storage and connection
tools. This immense data growth does not easily allow to useful information or orga-
nized knowledge to be understood or extracted automatically. This fact has led to the
start of Data Mining (DM), which is currently a well-known discipline increasingly
preset in the current world of the Information Age.
DM is, roughly speaking, about solving problems by analyzing data present in
real databases. Nowadays, it is qualified as science and technology for exploring
data to discover already present unknown patterns. Many people distinguish DM as
synonym of the Knowledge Discovery in Databases (KDD) process, while others
view DM as the main step of KDD [
16
,
24
,
32
].
There are various definitions of KDD. For instance, [
10
] define it as “the nontrivial
process of identifying valid, novel, potentially useful, and ultimately understandable
patterns in data” [
11
] considers the KDD process as an automatic exploratory data
