TESI DOCTORAL - La Salle

1.1. Knowledge discovery and data mining
Figure 1.2: Schematic diagram of the steps involved in the KD process (extracted from
(Fayyad, 1996)).
It is a commonplace that potentially useful and beneficial information patterns lie in
digital data repositories awaiting analysis (Witten and Frank, 2005). However, the concept
of analysis and its goals are highly local to the context it is applied (Fayyad, 1996).
Typical application scenarios can be as disparate as i) mining records of buyers’ choices
for creating marketing campaigns adapted to distinct customer profiles (Witten and Frank,
2005), ii) analyzing credit card transactions history of bank customers so as to detect possible
fraudulent operations from unauthorized users (Fayyad, 1996) or iii) locating and
cataloging geologic objects of interest in remotely sensed images of planets or asteroids
(Fayyad, Piatetsky-Shapiro, and Smyth, 1996).
Thus, be it either economic or scientific, there exists a great interest in replacing (or, at
least, augmenting) human analytic capabilities by computer-based means. The field of computer
science devoted to the extraction of useful patterns from data has been given different
names in the literature, such as information discovery, information harvesting or data archaeology
(Fayyad, Piatetsky-Shapiro, and Smyth, 1996), being knowledge discovery2 (KD)
and data mining (DM) the two most common denominations.
However, the use of KD and DM as synonymous concepts has been a matter of dispute in
the research community (Klosgen, Zytkow, and Zyt, 2002): while deemed equivalent by some
authors (Witten and Frank, 2005), others refer to KD as the whole process of extracting
knowledge from data, defining DM as the central constituting step of KD processes (Fayyad,
Piatetsky-Shapiro, and Smyth, 1996), as depicted in figure 1.2.
According to this latter standpoint (to which we adhere in this thesis), KD is defined
as the ‘non-trivial process of identifying valid, novel, useful and ultimately understandable
patterns in data’, whereas DM is ‘the application of specific algorithms for extracting patterns
from data’ (Fayyad, Piatetsky-Shapiro, and Smyth, 1996). By ‘extracting patterns
patterns from data’ we refer to making any high-level description of a set of data, e.g. fitting
a model to data or finding structure from it (Fayyad, Piatetsky-Shapiro, and Smyth,
1996). Thus, according to this point of view, KD and DM constitute what could be called
2 Although this discipline was originally named KDD —for Knowledge Discovery in Databases (Piatetsky-
Shapiro, 1991)— in this work we assume that operations are conducted on a flat file extracted from the
database, i.e. we remove the second D in KDD and focus on the knowledge discovery process.
4