KnowledgeMiner Insights Data Mining for Mac OS X: Ivakhnenko 100th Anniversary Giveaway
Marking the 100th anniversary of the birth of cybernetics pioneer Prof. Aleksey Ivakhnenko, KnowledgeMiner Software announces a 100% discount on its entry-level Insights Touch Edition, downloadable free only on September 18, 2013.
Sep 16, 2013. Berlin, Germany - Marking the 100th anniversary
of the birth of cybernetics pioneer Prof. Aleksey Ivakhnenko, KnowledgeMiner Software today announces a Insights 2.0.3 Giveaway 100% discount on its entry-level Insights Touch Edition, downloadable free only on September 18, 2013. Ivakhnenko is recognized internationally as the author of the Group Method of Data Handling (GMDH), implemented in Insights software, an inductive, self-organizing, modeling technology, which has revolutionized knowledge discovery from data and data mining.
Having hosted Norbert Wiener, the father of cybernetics, after a major conference in Kiev, Ivakhnenko authored more than 40 books and 500 scientific papers over his 65-year career, making significant contributions to informatics, cybernetics, artificial intelligence, intelligent control, and modeling.
Highlights:
- 2013 is the 100th anniversary of the birth of cybernetics pioneer Prof. Aleksey Ivakhnenko
- Insights 2.0.3 Touch Edition will be available free for one day only, September 18, 2013
- Ivakhnenko is the creator of the unparalleled Group Method of Data Handling inductive, self-organizing, modeling technology and a pioneer in predictive control theory
- He initiated the development of a proven noise immunity theory for inductive modeling not found in any other data mining technology as a key feature of data analysis
- It has been mathematically proven that models obtained by self-organizing, inductive modeling predict more accurately on noisy data than models based on physical principles
- The ability to continuously make predictions is the core of human intelligence
- GMDH, as implemented in Insights for OS X, brings conventional data mining to a new level of sophistication and applicability
- Insights is currently used by NASA, Boeing, MIT, Columbia University, Merck, Mobil, Notre Dame, Pfizer, Apple, and others
- Insights supports multi-core processing and includes a complete User Guide
In his 1981 paper, Stanley J. Farlow effectively summarized the relevance of Ivakhnenko's GMDH, "A major difficulty in modeling complex systems in such unstructured areas as economics, ecology, sociology, and others is the problem of the researcher introducing his or her own prejudices into the model. Since the system in question may be extremely complex, the basic assumptions of the modeler may be vague guesses at best. It is not surprising that many of the results in these areas are vague, ambiguous, and extremely qualitative in nature.
"It was for this reason that in the mid 1960's the Ukrainian mathematician and cyberneticist, A.G. Ivakhnenko, introduced a method that allows the researcher to build models of complex systems without making assumptions about the internal workings. The idea is to have the computer construct a model of optimal complexity based only on data and not on any preconceived ideas of the researcher; that is, by knowing only simple input-output relationships of the system, Ivakhnenko's GMDH algorithm will construct a self-organizing model that can be used to solve prediction, identification, control synthesis, and other system problems."
Frank Lemke, President of KnowledgeMiner Software, provides a more detailed analysis,
"Ivakhnenko introduced the idea of external information into modeling by subdividing a dataset into training and testing data sets for model evaluation, and he initiated and headed the development of a noise immunity theory for modeling, which is key and still unique in data mining to allow true knowledge extraction from data by inductively, systematically, and automatically evolving an optimal complex model by employing parameter and structure identification. In this way, the basic problem in experimental systems analysis of avoiding models that fit to data by chance, and therefore have bad descriptive and predictive power, has been solved."
Today, self-organizing inductive modeling is a proven and highly efficient knowledge extraction technology. It is available in the Insights app, which is the only implementation of advanced GMDH algorithms for Mac OS X. Recent advances in research and development have made possible parallel implementations, multi-level self-organization for modeling high-dimensional data sets containing many thousands of input variables, cost-sensitive modeling, and new model evaluation techniques to improve the reliability and applicability of models. The software has been employed successfully in various fields, from image recognition over biomarker detection and QSAR modeling, wastewater management and reuse questions, to Global Warming and micro and macro-economic forecasting problems.
Language Support:
- English, German, Spanish
System Requirements:
- OS X 10.7 or later
- Any Mac with 64-bit CPU
- Minimum screen resolution of 1280 x 768
- For Excel support, Excel versions 2011 or 2008
- 74.5 MB
Pricing and Availability: Insights 2.0.3 Touch Edition, regularly $80.00 (USD), will be available free for one day only, September 18, 2013, directly from the KnowledgeMiner Software website. Also available are Insights Advanced for $325, and Insights Professional for $1,950. Educational pricing is provided on request.
KnowledgeMiner Software: www.knowledgeminer.eu
Insights 2.0.3 Giveaway: www.knowledgeminer.eu/store.html
Ivakhnenko Photo: www.knowledgeminer.eu/img/Ivakhneko.png
Ivakhnenko with Wiener: www.knowledgeminer.eu/img/Ivakhneko_Wiener.png
Most popular last 30 days
Most viewed last 30 days
- 50+ Data Science and Machine Learning Cheat Sheets - Jul 14, 2015.
- R vs Python for Data Science: The Winner is ... - May 26, 2015.
- Top 20 Python Machine Learning Open Source Projects - Jun 1, 2015.
- 9 Must-Have Skills You Need to Become a Data Scientist - Nov 22, 2014.
- Deep Learning and the Triumph of Empiricism - Jul 7, 2015.
- Can deep learning help find the perfect date? - Jul 10, 2015.
- Top 10 Data Analysis Tools for Business - Jun 13, 2014.
- Stop Hiring Data Scientists Until You Are Ready for Data Science - Jul 17, 2015.
Most shared last 30 days
- 50+ Data Science and Machine Learning Cheat Sheets - Jul 14, 2015.
- Deep Learning and the Triumph of Empiricism - Jul 7, 2015.
- Deep Learning Adversarial Examples - Clarifying Misconceptions - Jul 15, 2015.
- Data Science and Big Data: Two very Different Beasts - Jul 6, 2015.
- Impact of IoT on Big Data Landscape - Jul 29, 2015.
- Stop Hiring Data Scientists Until You Are Ready for Data Science - Jul 17, 2015.
- Can deep learning help find the perfect date? - Jul 10, 2015.
- R, Python users show surprising stability, but strong regional differences - Jul 14, 2015.