Casestudie Breakdown prediction Contell PILOT - Transumo

implemented. These implementations are characterized by the type of text input and
the estimated output. 59
A very popular example for text mining is the automated collection of e-mail- and
postal addresses from internet pages. The text mining algorithm has to identify these
mentioned addresses as well as links to other pages to be able to continue
searching. But as this data mining approach can only be applied to textual data, this
diploma thesis will not go into further detail.
Association rule mining is a multi criteria approach. Its purpose is the explorative
discovery of dependencies between several items. The association rule mining is
based on statistical correlation analysis. But as this is a multi criteria approach, it
needs at least two different measures as input. 60
The above mentioned example of beer and potato chips is a typical assignment but
an appliance to temperature monitoring data does not seem to be promising,
especially because the only possible information gain is a correlation between door
openings and temperature behavior, which is generally known already. Hence, this
data mining approach will also be left out within this diploma thesis.
Prediction methods like regression and time series analysis are already introduced.
The setting of data mining offers an additional approach, the so called artificial neural
networks. These networks will be introduced and reviewed in the succeeding
subsections.
Clustering is an approach that scans large datasets and tries to identify different
kinds of groups, which are previously unknown. Clustering is often used as a first
step to apply other data mining methods to the identified groups ([Martin98], p. 269).
The example of adapted sales promotion could be achieved by the use of clustering.
Therefore, groups are determined automatically, that divide the customer’s behavior
best (e.g. a separation by special interest or buying behavior) ([Lusti02], p. 261).
Classification is similar to clustering. The main difference is the already existing
knowledge of the classes (e.g. “creditworthy” vs. “not creditworthy”). An easy but
basic approach is the so called rule induction. New rules are either created by
experts or by an analysis of historical data ([Gentle02], p. 237-238). Automated
59 See (e.g. [Multhaupt00], chapter 3-4) for details
60 See (e.g. [Wittenberg98], p. 161-165) for details
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