Cutting Edge Macintosh Technology for DataMining now uses AppleScript

KnowledgeMiner is a software application being used by NASA, Boeing, MIT,
Columbia, Notre Dame, University of Hamburg, Mobil Oil, Pfizer Inc., Dean &
Company and many other corporations, universities, research institutions
and individuals around the world. Because it is a datamining technology it
can be used in any field of human enquiry to reveal new and previously
unknown relationships in data.

KnowledgeMiner is the first choice in datamining because it provides the
most objective, easiest, fastest and least expensive data mining
technologies in the world. Also as it learns about a new dataset it
generates equations on the fly that model that data. It is knowledge
extraction in its most advanced form.

Version 3.2
– Now AppleScriptable
– Can protect/unprotect cells

KnowledgeMiner can be downloaded from:
Included datasets and examples range from prediction of global temperature,
stock market trends, medical diagnosis, failure of materials (like the
Challenger Space Shuttle O-Ring), to party affiliation in the US congress.
The many examples included with KM show its power to work on human issues.

KnowledgeMiner from Script Software is the only dataminer that has GMDH
(group method of data handling), Analog Complexing and Fuzzy Rule Induction
in one application. As a prediction tool and also for educational purposes
KnowledgeMiner has no equal.

KnowledgeMiner is a powerful, easy-to-use modeling and prediction tool
which was designed to support the knowledge extraction process from data in
a highly automated way. It works using three advanced self-organizing
modeling technologies: Group Method of Data Handling (GMDH), Analog
Complexing and Fuzzy Rule Induction. This is the first time that all of
these algorithms have been available in one place on any computer platform.

Built on the cybernetic principles of self-organization, KnowledgeMiner
brings high-end modeling capabilities to your desktop without the need for
an expert. It can learn completely unknown relationships between the
outputs and inputs of a given system in an evolutionary way from a very
simple organization to an optimally complex one by itself.

Why is KnowledgeMiner needed? There are two main reasons.

First because of the explosive growth of many business, government,
scientific and personal databases. The flood of information has far
outpaced our ability to interpret and digest this data. Huge databases like:

– the human genome project
– Mobil oil exploration info (100 terabytes)
– NASA Earth Observing System (50 gigs/hour) and many others are all rapidly
expanding the volume of info that is available to us.

Second because now there is a strong demand for a new generation of tools
and techniques for automated and intelligent database analysis both on
large and small datasets. Coupled with a growing desire by non-specialists
for easy to use tools which don’t require a lifetime of experience and
knowledge to operate.

KnowledgeMiner has been used successfully for analysis and prediction in
the fields of finance, economics, imaging, ecology, health, biotechnology,
chemistry, math and many others. Professionals in many areas can benefit by
downloading and trying out the examples then using this tool to mine their
own field for new relationships.

In comparison with statistical modeling tools and neural networks
KnowledgeMiner stands out as easier, faster and more applicable to a wide
range of real-world problems since it creates the model structure or
network topology automatically without predefinition. This makes
KnowledgeMiner the least expensive and most effective modeling and
prediction tool available on any platform.

KnowledgeMiner is a Mac only tool. Those without a Mac can either buy a
KnowledgeMiner workstation (any PPC Mac) or use a Mac emulator on their PC
like the one at This strategy allow the programmers to focus
their energies on supporting the fast PPC and PPC/Altivec processors to add
more features and new algorithms.

Data mining, knowledge discovery, and decision support are becoming
increasingly important for all areas of human activity. As an example look
at El Nino. El Nino is now described as a minute temperature change in the
ocean off of South America which strongly influences global weather. A
couple years ago we were all very impressed by the power of El Nino but
until recently that data was buried in a mountain of other climatic data.
How many other powerful influences on weather, politics, sociology,
economy, energy, health, history, crime, etc. are hidden in the vast store
of data we already have?

Specific questions like how national economies are interconnected and
affected by local crises, how water quality and temperature is influenced
by the environment and vice versa, how the population of a country or the
world will change, how cancer can be detected and understood better or how
credit card fraud can be detected are only a few of the applications that a
tool like KnowledgeMiner can be used for.

Many people have wanted to learn more about self-organizing modeling and
the basic ideas behind KM’s modeling methods. Frank Lemke and Prof. Mueller
have just finished a book called “Self-Organising Data Mining. An
Intelligent Approach To Extracting Knowledge From Data

Real World Applications
Future Technologies”

This book covers several areas of knowledge discovery and introduces a
spectrum of parametric and nonparametric modeling methods that are
scheduled to be implemented in KnowledgeMiner – many of them for the first
time on any computer platform. Two important new algorithms are Objective
Cluster Analysis and the other is Self-Organizing Fuzzy-Rule Induction. The
latter is designed as a tool for describing fuzzy processes qualitatively.
The book is available now on our website as a downloadable Adobe Acrobat

Also the KnowledgeMiner discussion area is available at:

Here are some user comments about KnowledgeMiner.

“I like KnowledgeMiner because its algorithm does not make any assumtions
on the underlying data; well, at least not during the initial
model-building phase. I also like the fact that it generates sets of
equations that the user can review with detailed understanding of the
interactions and dependencies of each variable. Also, the algorithm(s)
behave surprising well under extreme conditions for certain complex
dynamical systems. Congratulations for your excellent work. I was eagerly
awaiting the PPC version. Thank you.”
Alexis, Pfizer Inc.

“I have purchased your program KnowledgeMiner and have had some time to use
it. My research is in artificial intelligence applications in clinical
medicine at the University of Western Ontario in London, Canada. I have so
far used backward error propagation and probalistic ANNs for outcomes based
research. I also have some experience with fuzzy decision theory and expert
systems. Your program looks interesting and has some advantages over my
current modelling software (ie. NeuroSMARTs, Brainmaker and Neuralyst). …
I wish to congratulate you on your very promising software.” Wayne,
Associate Professor of Medicine, Division of Cardiology, University of
Western Ontario, London, Canada

“I’m a physicist by training, working as a radar engineer on some cutting
edge target recognition/classification technologies. I am now using KM to
circumvent all the past pattern recognition algorithms which have been
years (and millions of $) in development by the armed forces. Although I am
just now starting to use KM in this application, my initial indications are
that KM is providing a more robust,complete and more accurate
classification capability than any of the previously used algorithms, and
with comparatively no effort on my part”
Herb, Vista Technologies, Inc.

“KnowledgeMiner is the only product that I have found that makes it easy to
try non-standard equation formats on a data set. Many standard regression
tools are as easy, but they limit you to a small set of potential
relationships. KnowledgeMiner combines spreadsheet-like set up with an
algorithm that doesn’t “over fit” the model. Also, the output is in a
readily usable format (e.g. not C++ code).” Ware, Dean & Company

“Alpine skiing and athletic french federation have contacted my laboratory
to build a profile of their elite athletes. In this case KnowledgeMiner
helped me save a lot of time and gave me models on the most important
variables, and pointed out the less relevant.” Fabrice, Laboratoire de
Physiologie, Faculte de Medecine

Thank You

Frank Lemke
Julian Miller