|
Data Mining and Consulting
Services
|
KnowledgeMiner's self-organizing data
mining technologies have a long history. The software
integrates results from over 30 years of research and
application from different sciences like cybernetics,
systems theory, computer science, and mathematics making it
a most objective, fast and systematically working knowledge
discovery tool. However, only a fraction of existing
technologies and concepts have been implemented in the
publicly available KnowledgeMiner software tool yet. It is
evolving software, but you can profit from the experience of
our renowned experts, and from our in-house tools and
innovative technologies right now in using our services.
Recent years have shown that self-organizing data mining
technologies if applied properly is the easiest, fastest,
and a most reliable way to mine data and to
extract some knowledge in form of equations, rules,
patterns, or cluster that describe that data. It is exactly
that discovered knowledge that makes the difference and that
is key to improve model results and to gain some insights
into investigated "black boxes". Have a look at
various
applications.
If you have a one-time modeling, classification or
prediction problem or if you just want to save your time
necessary for learning how to use a software tool and how to
get the most out of your data, our data mining service may
be what you have been looking for. Based on a nondisclosure
agreement you send us your data and you will get back
shortly a set of models that
- are composed of relevant inputs. Our
algorithms automatically select important inputs during
self-organization of models.
- describe your data appropriated. Based on the
data's noise level, e.g., we decide which algorithms are
suited to model the data. Self-organising data mining not
just only models the input-output behavior like common
Neural Networks do, but they also always provide a
corresponding explanation of the data. This can be:
- regression models of static systems y=f(x)
or dynamic systems y=f(x, t)
- fuzzy/logic IF-THEN rules composed of
linguistic variables and AND, OR, NOT operators
describing static or dynamic systems
- similar patterns/cases for prediction or
classification problems identifying similarities in
the data
- cluster of similar data samples or
variables
- generalize well on new data. Our
self-organizing modeling technologies have a built-in
mechanism that generate optimal complex models to avoid
overfitting with respect to the data's noise level and
the information used to train the models. On a most
possible likelihood, this ensures that the generated
models also do well on new data.
- are bundled to a combined solution. Usually, a
model is a simplified, one-sided reflection of reality
only. Combining different models from different data
mining technologies can result in more certain and more
robust object description. Therefore, we will give you a
bundle of self-organized models into your hands.
Results and models will be summarized in a report and
will become a property of you. Interpretations, suggestions,
and conclusions of the results are not included in the data
mining service. Check out consulting for this. Send
a short note for further information.
If you are interested in a more complete project
based cooperation including data preprocessing, feature
extraction, data mining, interpretation and implementation
of generated models, or even programming, ask for our
consulting
service.
Prof. Dr. Johann-Adolf Mueller
- more than 20 years of experience in inductive
modeling and simulation of complex systems
- author of many papers and books on self-organizing
modeling including "Self-Organising
Data Mining" book
- runs several workshops on application of
self-organizing data mining in economy and ecology using
KnowledgeMiner and other DM tools
- visiting professor at the Chengdu University of
Sciences and Technology, China
Prof. Dr. Aleksey G. Ivakhnenko
- developed the Group Method of Data Handling (GMDH) in
1968, an advanced Statistical Learning Network approach
based on inductive self-organisiation. It has been
adapted and improved by renowned scientists from the USA
(Barron, Elder, e.g.), Japan (Kondo, Tamura, Sugeno),
China (Wang), and several European countries.
- corresponding member of the National Academy of
Sciences of Ukraine. Advisor of the Control Systems
division of the famous Glushkov Institute of
Cybernetics.
- author of about 15 monographs and 300 papers on
mathematical modeling and pattern recognition of complex
systems
Dr. Olaf Jaeckel
- has been working in the field of GMDH Neural Networks
since 1995
- contributes to the evolution of KnowledgeMiner
- doctoral thesis on quality-oriented process control
by iterative structure synthesis using GMDH
- works in several industry projects on statistical
processing and modeling of data for quality assurance
purposes
Julian Miller
- Managing Director of Script Software
International
Frank Lemke
- has been developing the KnowledgeMiner software
- author of several papers and the "Self-Organising
Data Mining" book
- consulting experience in self-organizing data mining
technologies for several years including GMDH NNs, Nets
of Active Neurons, self-organizing Fuzzy Rule Induction,
Analog Complexing pattern recognition for forecasting,
classification and clustering
|
