# Data-Mining

Data-Mining is a process that identifies and presents patterns, structures, relationships, trends and peculiarities through the systematic application of numerical algorithms to a database of measurement or calculation. It is therefore typically used when the question is not precisely defined or the choice of a suitable statistical model for the existing data is unclear. OptiY has new and comprehensive algorithms from Machine Learning, Artificial Intelligence, Optimization, Genetic Evolution, Combinatorics, Graph Theory, Numerical Statistics, etc. This makes it possible to find and solve previously undiscovered solutions for open questions by means of extended and novel analyses that have gone beyond standardized procedures.

With **data import/export wizard** you can import or
export the data in the form of ASCII text file or MS Excel file
quickly and easily. If there is a data error in a certain place, a
message is displayed to the user to correct it. The process chain
for the analysis is automatically generated in the workflow editor.
In addition, individual data processing steps can also be easily
created and supplemented graphically.

After the start of the data analysis, the **data visualization
and statistics** take place. Extensive graphical 2D and 3D
elements such as histogram, correlation matrix, scatter plot,
parallel chart, table, etc. are available. The operation is very
easy by drag and drop with the mouse click. Statistical moments and
distributions of the data can be read here. This makes possible to
check the database and to detect and correct any irregularities or
errors in advance. For dynamic data, OptiY can also perform a time
series analysis such as time and FFT frequency analysis.

The 3 important tasks for recognizing patterns, structures and
relationships include cluster analysis, regression and
classification. **Clustering** sorts the data to
different groups whose members have the same or similar properties.
**Regression** forms an analogous mathematical function
over the data in parameter space. On the other hand, **
Classification** has the task of mapping several states of
the data by using digital mathematical functions. This makes
possible to recognize and represent relationships between input and
output parameters in the form of metamodels. These metamodels are
the basis for further derived tasks such as prediction, sensitivity
studies, uncertainty analysis and optimization as well as robust
design. Thus, **prediction** about the future trend,
**pattern or object recognition** can also be made.

**Sensitivity analysis** answers questions about
reducing data complexity and explaining the cause-and-effect
relationship. All parts of the variations of individual input
parameters on the total variation of the target parameters are
statistically calculated on the basis of metamodel. This makes
possible to recognize important parameters and their interactions
and thus also to identify the causes of the problem.

**Uncertainty analysis** investigates unavoidable
scattering or uncertainties of the input parameters. From this, the
variation of the target parameters is calculated analytically and
accurately. Thus, it is possible to investigate the variation of a
process at one operating point for a specification, whether the
required safety and robustness of the process is guaranteed?

**Optimization** looks in the parameter space for the
best operating point where the objective function is best met for
the user-defined requirements. All you have to do is formulate
objectives and OptiY will automatically find optimal parameters. For
example, it makes sense to find an optimal operating point for a
process where the energy loss is the lowest.

**Robust Design** is an optimization of the uncertainty
analysis including the parameter scattering or uncertainties. An
optimal operating point for a specification is sought, in which the
required safety and robustness of the process is always guaranteed
despite the unavoidable parameter scattering. This advanced
technology is part of the innovative core in OptiY.

With the Data-Mining Edition you can also generate the data for the
design of experiments. Various methods such as Monte-Carlo,
Latin-Hypercube, Sobol, Orthogonal Array, Central Composite, etc.
can be used for **statistical design of experiments**.
Correlations between the parameters or conditions for the data set
can be easily mapped. Another innovation of OptiY is the **
adaptive design of experiments**. On the basis of an existing
database at the beginning, a metamodel is first formed. Further
measuring points in the parameter space are proposed in order to
achieve more accurate metamodels or better results of data analysis.
It is the most efficient design of experiments with fewer measuring
points. This saves a lot of effort, time and costs for expensive
experiments.

# Case Studies