Data Analysis Machine Learning and Applications Episode 2 Part 4 doc

A Pattern Based Data Mining Approach 333

2. Science converges. Concepts in one area of science is applicable in another area.

Patterns support these processes. This potential is comparable to the promises of

Systems Theory.

3. Decision for a specific algorithm can be postponed to later stages. A solution

path as a whole will be sketched through patterns and algorithms need only be

filled in immediately prior to processing. Using differnet algorithms in places

will not invalidate the solution path, creating “late binding” at the algorithm

level.

Current Data Mining applications occasionally provide the user with first traces

of pattern based DM. Figure 5 shows the example of Bagging of Classifiers within

the TANAGRA project and its graphical user interface (Rakotomalala (2004)). Bag￾ging cannot be described with a pure data flow paradigm, rather a nesting of a clas￾sifier pattern within the bagging pattern is needed. This nested structure will then be

pipelined with pre- and postprocessing patterns.

Fig. 5. Screenshot of Tanagra Software

Further steps in our project are to

• collect a list of patterns which are useful in the whole knowledge dis￾covery process and data mining (list will be open-ended).

• integrate these patterns into data mining software to help design ad-hoc

algorithms, choose an existing one or have guidance in the data mining

process.

• develop a software prototype with our pattern and make experiments

with users: how it works and what are the benefits.

334 Boris Delibašic, Kathrin Kirchner and Johannes Ruhland ´

References

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