KnowledgeMiner 1.4 for OS X: Mine. Extract. Predict. Identify. Simulate.
Berlin, Germany - KnowledgeMiner Software today is pleased to announce KnowledgeMiner 1.4 for OS X, an update to its critically acclaimed application that takes conventional data mining to a new level of sophistication and applicability. Users in nearly any field can employ the easy-to-use software to analyze noisy data sets and build powerful models, which can be used to help to gain new insights into complex phenomena, predict future behavior, simulate "what if" questions, and identify methods of controlling processes. KnowledgeMiner is currently used by NASA, Boeing, MIT, Columbia University, Merck, Mobil, Notre Dame, Pfizer, Apple, and many other corporations, universities, research institutes, and individuals worldwide.
KnowledgeMiner is a professional, yet convenient tool for building predictive models from data autonomously. Taking observational data that describes a problem, system, or process, the software constructs a working mathematical model. Compatible with data stored in a variety of popular formats (e.g., Microsoft Excel), its AI-powered, self-organizing, modeling algorithms allow users to easily extract new and useful knowledge to support decision-making.
Whether applied to sales prediction, financial and resource planning, engineering problems, climate change, health or life sciences related questions, or mining collections of data from government agencies, KnowledgeMiner opens up a wealth of new possibilities to individuals, small business owners, and scientists that were previously available only to large entities that could afford expensive data mining applications.
- Brings high-performance Personal Knowledge Mining to users with unprecedented ease of model building and deployment - takes full advantage of the computing power of your Mac;
- Hides all complex processes of knowledge extraction, model development, dimension reduction, variables selection, noise filtering, and model validation from the user;
- Self-organizes linear or nonlinear, static or dynamic regression models and model ensembles - generates the equation that describes the data;
- Checks if, and the extent to which, the developed model reflects a valid relationship or if it just models noise - employs advanced validation methods based on higher-dimensional modeling;
- Live Prediction Validation technology - for the first time, gives direct information about model stability for the given input values;
- Generated analytical models can be used for Status Quo or What If prediction, analysis, simulation, or optimization problems;
- Optionally, it implements models and model ensembles in Excel - model export requires Microsoft Excel for Mac 2011 or 2008.
Clearly, we are facing an ever-widening spectrum of complex problems, which require analysis and action. However, the means and adequate models for understanding, simulating, and predicting such problems often do not exist. This is particularly the case in many real-world, socio-economic, eco-economic, ecological, biological, and energy challenges. To fill this knowledge gap, new, inductive learning, self-organizing modeling methods have to be applied, which can help reveal the missing, implicit relationships in complex systems in an adaptive, fast, reliable, and objective way.
Complex systems demand that we abandon the classical hard approach, based on the assumption that the world can be understood objectively and that knowledge about the world can be validated through empirical means. Complex problems require a soft paradigm, which can better describe vagueness and imprecision. This approach is based on the observation that humans often have an incomplete and vague understanding of the nature of the world, but despite this fact, they are capable of resolving new and unpredicted situations through knowledge and experience.
The efficiency and reliability of this self-organizing modeling approach has been proven in the past decades by numerous practical applications. More recent ones include the modeling and prediction of harmful health and environmental effects of chemicals such as carcinogenicity, mutagenicity or bioaccumulation for regulatory purposes and consumer safety to minimize animal tests, which was done in cooperation with the Istituto Mario Negri, Italy, the U.S. Environmental Protection Agency or the UK Food and Environment Research Agency.
Another real-world example that shows the power and value of KnowledgeMiner is an ex ante forecast of monthly global mean temperatures until November 2017, which is based on models that were developed back in May 2010 and which is available for live actual vs predicted comparison.
"I invite everyone interested in KnowledgeMiner to download our free Demo version," commented Frank Lemke of KnowledgeMiner Software. "Discover the true computing power of your Mac with KnowledgeMiner's implementation of 64-bit parallel computing, with support for vector processing, and multi-core and multi-processor machines."
- English, Spanish, and German
- OS X 10.7 Lion or higher
- 64-bit CPU
- 156.7 MB
- Minimum screen resolution of 1280 x 768 pixel
- For Excel support, Excel versions 2011 or 2008
Pricing and Availability:
KnowledgeMiner 1.4 starts at 80 Euros (approximately $119 USD), and it is available worldwide exclusively through the KnowledgeMiner Software website. A free Demo version is available for download. Review copies are available on request.
Located in Berlin, Germany, KnowledgeMiner Software was founded in 1993 by Frank Lemke. The company is active in research, development, consulting, and application of unique, self-organizing modeling and knowledge discovery technologies. KnowledgeMiner Software has been doing consulting in model development and prediction of toxicological and eco-toxicological hazards and risks of chemical compounds from experimental data for regulatory purposes within REACH, and it has participated in three international research projects funded by the European Commission related to QSAR modeling and model evaluation. Other fields of activity have been climate change related modeling and prediction problems, sales and demand predictions, macro- and micro-economic modeling problems like national economy and balance sheet prediction, energy consumption analysis and prediction, medical diagnosis of diseases, and wastewater reuse problems.
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