Databases Reference
In-Depth Information
What are the differences, if any, between the existing researches and the re-
search on the foundations of data mining? The study of foundations of data
mining attempts to answer these questions.
The foundational study is sometimes ignored or underestimated. In the
context of data mining, one is more interested in algorithms for finding knowl-
edge, but not what is knowledge, and what is the knowledge structure. One is
often more interested in a more implementation-oriented view or framework
of data mining, rather than a conceptual framework for the understanding of
the nature of data mining. The following quote from Salthe [16] about studies
of ecosystem is equally applicable to the studies of data mining:
The question typically is not what is an ecosystem, but how do
we measure certain relationships between populations, how do some
variables correlate with other variables, and how can we use this
knowledge to extend our domain. The question is not what is mito-
chondrion, but what processes tend to be restricted to certain region
of a cell [page 3].
A lack of the study of its foundation may affect the future development of the
field.
There are many reasons accounting for such unbalanced research efforts.
The problems of data mining are first raised by very practical needs for find-
ing useful knowledge. One inevitably focuses on the detailed algorithms and
tools, without carefully considering the problem itself. A workable program
or software system is more easily acceptable by, and at the same time is more
concrete and more easily achievable by, many computer scientists than an
in-depth understanding of the problem itself. Furthermore, the fundamental
questions regarding the nature of the field, the inherent structure of the field
and its related fields, are normally not asked at its formation stage. This is
especially true when the initial studies produce useful results [16].
The study of foundations of data mining therefore needs to adjust the
current unbalanced research efforts. We need to focus more on the under-
standing of the nature of data mining as a field instead of a collection of
algorithms. We need to define precisely the basic notions, concepts, princi-
ples, and their interactions in an integrated whole. Many existing studies can
contribute to the foundational study of data mining. Here are two examples:
(a) Results from the studies of cognitive science and education are relevant
to such a purpose. Posner suggested that, according to the cognitive science
approach, to learn a new field is to build appropriate cognitive structures and
to learn to perform computations that will transform what is known into what
is not yet known [14]. (b) Reif and Heller showed that knowledge structure
of a domain is very relevant to problem solving [15]. In particular, knowledge
about a domain, such as mechanics, specifies descriptive concepts and rela-
tions described at various levels of abstraction, is organized hierarchically, and
is accompanied by explicit guidelines that specify when and how knowledge is
to be applied [15]. The knowledge hierarchy is used by Simpson for the study
