TESI DOCTORAL - La Salle

1.1. Knowledge discovery and data mining
Verrification
Goodness of
fit
HHypothesis
teesting
Analysis oof
variancee
Data miining
methoods
Prediction
Classific cation
Regres ssion
Discovery
Description
Clusteriing
Summarization
Figure 1.3: Taxonomy of data mining methods (adapted from (Maimon and Rokach, 2005)).
Next, depending on the goals of the KD process, a suitable data mining task must
be chosen. According to the taxonomy presented in figure 1.3, data mining methods can
be classified into two main groups: verification-oriented and discovery-oriented (Fayyad,
Piatetsky-Shapiro, and Smyth, 1996). In verification-oriented DM, the role of the system
is to evaluate an hypothesis proposed by the user, and this task is usually accomplished
by means of traditional statistical methods. In discovery-oriented data mining, the goal of
the system is to discover useful patterns in the data. In this work, we focus on this latter
family of DM tasks.
Among discovery-oriented data mining methods, one can distinguish between prediction
and description DM tasks. In prediction tasks, the goal of the system is to build
a behavioral model upon the data, whereas in description tasks, the system aims to find
human-understandable patterns that facilitate knowledge extraction from data.
According to figure 1.3, there exist two main prediction DM tasks: classification and
regression. In classification problems, the goal is to learn a mapping between the categories
in a known taxonomic scheme and a set of pre-classified objects, so that any unseen object
can be categorized into any of these predefined classes. The aim of regression tasks is to
learn a function that maps unseen data objects to a real-valued prediction variable.
As aforementioned, description-oriented DM methods focus on finding understandable
representations of the underlying structure of the data (Maimon and Rokach, 2005). One of
the most common descriptive DM tasks is clustering, which consists of identifying a finite
set of categories to describe the data with no previous knowledge (i.e. deriving a taxonomy
solely from the data). Another description-oriented data mining task is summarization,
whose aim is to find a compact description for a subset of data. To do so, summarization
techniques often make use of multivariate visualization methods.
Once the data mining task that fits the goals of the KD process is identified, there
comes the time to select the specific data mining algorithm to be applied. This selection
must take into account not only which models and parameters are the most appropriate
from an algorithmic viewpoint, but also the desired level of accuracy, utility, and intel-
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