Berlin, Germany – KnowledgeMiner Software today is pleased to announce the release and immediate availability of Insights 3.0 for Mac OS X, an update to their outstanding self-organizing, predictive modeling application. With Similar Patterns technology Insights 3.0 adds innovative forecasting to its adaptively learning modeling methods. Users in nearly any field without being an expert in modeling can analyze data sets and build powerful models, which help to gain new insights into complex phenomena, predict future behavior, simulate “what if” questions, and identify methods of controlling processes. Insights is used for predicting sales, production and demand, engineering problems, climate change, health or life sciences-related questions, or mining collections of data from government agencies.
* High performance adaptive learning knowledge mining with ease
* Similar Patterns sequential pattern recognition method for easy forecasting
* 64-bit parallel software
* Hides all complex processes, such as knowledge extraction, model development, and variables selection, from user
* Self-organizes data and models and generalizes the equation that describes the data
* Live Prediction Validation technology
* Implements models and model ensembles in Microsoft Excel
* Includes documentation, extra literature, sample data and models for several data samples
Taking observational data that describes a problem, system, or process, Insights constructs a working mathematical model. Compatible with data stored in a variety of popular formats (i.e. Microsoft Excel), Insights has AI-powered modeling algorithms that allow users to easily extract new and useful knowledge to support decision-making. Users can develop predictive models and model ensembles, along with a prediction interval from low to high-dimensional noisy data, of up to several hundreds of input variables. Insights can create a validated optimal complex analytical model that can be applied and exported to Excel for further analysis.
Since complex, fuzzy, or very noisy processes are hard to model and predict by known parametric modeling and data mining technologies, Insights features Similar Patterns self-organizing technology. Similar Patterns can be seen as a sequential pattern recognition method that predicts and qualitatively explains fuzzy processes inherently. This method is based on the assumption that every most recent period of time (reference pattern) of a given multi-dimensional time process has one or more analogous periods in history (similar patterns). If so, a forecast of the reference pattern can be obtained by transforming the known continuations of the similar patterns in history into continuations of the present state of the process. This means the observed process itself is used to forecast its most recent state by a nonparametric approach.
The Similar Patterns method implemented in Insights utilizes an inductive, self-organizing modeling approach and an advanced selection procedure to make it applicable to evolutionary (non-stationary) time processes. As a result, this unique modeling technology allows forecasting of complex time processes, such as market prices, market demand, or sales figures instantly.
“Insights 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 application,” says Frank Lemke, founder of KnowledgeMiner Software. “The ability to continuously make predictions from auto-associative past patterns is the core of human intelligence. Self-similarity is a phenomenon often found in nature such as the Golden Ratio. Our Similar Patterns technology now make these proven and powerful concepts available for solving forecasting problems in many fields.” There are 3 different editions of Insights with a host of features: Insights Free, Insights Advanced, and Insights Pro. Insights Free is perfect for high model accuracy on smaller identification and classification problems. Insights has been used in a host of solutions and publications including the diagnosis of fetal heart rate signals from a number of measurements, indoor temperature forecasting for energy management and control purposes, global warming and ozone concentration forecasting, and prediction of residuary resistance (hydrodynamics) of sailing yachts at the initial design stage.
* English, German, and Spanish
* OS X 10.7 or later
* Any Mac with 64-bit CPU
* Minimum screen resolution of 1280 x 768 pixel
* For Excel support, Excel versions 2011 or 2008
* 82.8 MB
Pricing and Availability:
Insights 3.0 is available as a free version exclusively from the KnowledgeMiner Software website. Insights 3.0 Advanced and Pro, as well as academic versions, can be purchased by contacting KnowledgeMiner Software directly. 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 self-organizing modeling and knowledge discovery technologies. It developed and implemented a number of original technologies for validation of inductively built data mining models. KMS 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 participated in three international research projects funded by the European Commission related to QSAR. 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, and wastewater reuse problems. Copyright (C) 2013 KnowledgeMiner Software. All Rights Reserved. Apple, the Apple logo, Mac OS X and Macintosh are registered trademarks of Apple Inc. in the US and/or other countries. Other trademarks and registered trademarks may be the property of their respective owners.