Information Technology Reference
In-Depth Information
knowledge
Patterns
transformed
data
preprocessed
data
target data
data
multiple & heterogeneous
data sources
Fig. 3.1
The KDD process and its core step data mining, adapted from Fayyad et al. (
1996
)
3.1 Data Mining for CMA
According to Fayyad et al. (
1996
, p. 39) data mining refers to one particular step in
the overall process of discovering useful knowledge in data (Fig.
3.1
).
Data mining
is the application of specific algorithms for extracting patterns from data. [
]
The additional steps in the knowledge discovery in databases (KDD) process, such as data
preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and
proper interpretation of the results, are essential to ensure that useful knowledge is derived
from the data. Blind application of data-mining methods (rightly criticized as data dredging
in the statistical literature) can be a dangerous activity, easily leading to the discovery of
meaningless and invalid patterns.
...
3.1.1 Defining Movement Mining
Miller and Han (
2009
, p. 3) build on such early definitions of KDD and data mining
when they outline the fundamentals of Geographic Data Mining and Knowledge
Discovery. In their words, data mining involves distilling data into information or
facts about the domain described by a database. KDD by contrast is then the higher-
level process enriching such found information or facts into knowledge through
interpretation of information and its integration with existing knowledge about the
domain. This notion of distilling data into information and further into knowledge
complies very much with Anthony Galton's call to bridge the gulf between low level
observational data and the high-level conceptual schemes in which humans think and
understand geographic phenomena (Galton
2005
, p. 300). The key building blocks
