New Mac App KnowledgeMiner (yX) for Excel To Help Predict the Future

in Financial (E)

[prMac.com] Denver, Colorado - KnowledgeMiner Software in partnership with Intel and Microsoft has released a major new data mining application for the Mac called KnowledgeMiner (yX) for Excel. KnowledgeMiner (yX) for Excel takes advantage of multi-core processors thru the use of parallelization to achieve speeds more then 600 times faster then previously.

Frank Lemke the creator of (yX) said "Speed is always a big factor in data mining. Our previous product KnowledgeMiner was quite remarkable but (yX) for Excel amazed even us with its speed. We want to thank Microsoft and Intel for their help in developing this product". (yX) is one of very few applications that currently take advantage of multiple cores on the Mac and the only Mac data mining application to do so.

The goal of using KnowledgeMiner (yX) for Excel is to obtain useful knowledge from large collections of data. The discovered knowledge can be equations describing properties of the data, frequently occurring patterns, clusterings of the information, etc. The technology for data collection has made it easy to create massive stockpiles of data and these are growing rapidly. On the other hand the technology of data analysis has lagged far behind and tends to be time consuming and expensive. KnowledgeMiner (yX) for Excel is aimed at making the process faster and easier.

Spreadsheets are great at concentrating, displaying and manipulating data but standard spreadsheet functions don't reveal anything new. (yX) goes a big step further and extracts information from spreadsheet data, that the users did not already know. (yX) shows relationships that were previously unknown. (yX) can give equations that describe the data. (yX) is a tool for predictive analytics. The predictive analytics in (yX) uses a variety of techniques/algorithms from statistics and data mining to analyze current and historical data then makes predictions about future events. (yX) starts with raw data which it converts to information. This information is then converted into knowledge about historical patterns and future trends.

KnowledgeMiner (yX) finds correlations or patterns among dozens of fields in Excel spreadsheets. The relationships, correlations, or patterns amongst all this data can provide researchers new insights. One included example is the analysis of earth temperatures and CO2 in the atmosphere which shows a strong correlations. This in turn can lead to predictions about the future. If we don't like that future then we can propose ways to a different future and armed with that new knowledge change habits, purchases, technologies, investing, government, etc. to move to the future we like better. Instead of reading a headline about global warming a KnowledgeMiner user can look at the environmental data themselves and see the patterns and make the predictions.

Another recent area of success for KnowledgeMiner is in prediction of environmental repercussions of toxicity residues from chemical compounds (especially pesticides) This was done based solely on the compounds' chemical structure. This may sounds dry and technical but this single use of KnowledgeMiner has ended up saving the lives of thousands of animals a year in Europe that previously were used to as the front line to test the toxicity of these compounds.

KnowledgeMiner is being used by NASA, Boeing, MIT, Columbia, Notre Dame, University of Hamburg, Mobil Oil, Pfizer, Dean & Company, Demetra and many other corporations, universities, research institutions, projects and individuals around the world. Because it uses knowledge discovery via data mining it can be used in any field of human inquiry to reveal new and previously unknown relationships in data.

The recent financial turmoil is due to a lack of predictive insights into data that was already there. Governments and many companies are kicking themselves for this costly lack of insight. KnowledgeMiner (yX) for Excel could have helped all these organizations to see more clearly the results of their decisions and investments via predictive analytics and saved them billions of dollars.

Data mining is a great way to create new business opportunities. KnowledgeMiner (yX) for Excel makes it possible for anyone who can use a spreadsheet to do predictive analysis that previously was only available to governments and large corporations. This revolution in data mining allows individuals to use the past to predict the future and achieve better business results.

KnowledgeMiner has as a foundation its own unique set of inductive learning and self-organizing modeling technologies: GMDH Neural Networks, Fuzzy Rule Induction, and Analog Complexing pattern recognition for time series prediction, classification, modeling, clustering, and diagnosis of complex ill-defined systems. Multileveled self-organization and validation approaches make it not only possible to generate reliable individual and hybrid models in an objective way, but also provides an explanation component on the fly in the form of algebraic or difference equations, fuzzy rules, or a set of similar patterns. This extracted knowledge can be directly used to improve model results, to get new insights into the system, and to aid decisions.

KnowledgeMiner is most often purchased and used for stocks, currencies, and financial trading. But KnowledgeMiner is useful in every field of research and has been used with great success in recent projects in cancer research, prediction of wastewater pre-precipitation and wastewater reuse, to identify walking gait abnormalities for persons wearing prosthetic legs, analyzing medical data obtained from observing eye movement of children both healthy and children that display reading abnormalities, modeling and prediction of regional economies and related economic problems, problematic pharmaceutical manufacturing processes, researching various aspects of language teaching and learning, modeling China's macro economy for the government, and discovering the relationship between altered MR intensities in the caudate nucleus and patient disability in multiple sclerosis, etc. These all show that KnowledgeMiner can be applied in practically all fields of research.

KnowledgeMiner contains many pre-modeled examples from various application areas, several sample scripts for program-to-program communication, a sample KDD workflow applescript, TransformModel, and a Business Intelligence Workflow Case Study.

KnowledgeMiner (yX) for Excel also includes a PDF copy of the book called "Self-Organising Data Mining. Extracting Knowledge From Data." by Prof. J.-A. Mueller and Frank Lemke. This book is the easiest way to learn about data mining, self-organizing modeling and the basic ideas behind KnowledgeMiner.

Features
* Fast; especially on Intel Macs with many cores and lots of memory
* Universal Binary. Native on Intel and PowerPC, perfect for Leopard (10.5) and beyond
* Written in Cocoa and Objective C, to access the latest Mac OS X features for fast and rock-solid development and upgrades
* We listened to users and implemented the most requested features
* Reliable, compatible, powerful, inexpensive, useful, very practical and constantly being improved and updated
* Works directly in Microsoft Excel, unquestionably the lingua franca for finance and the world of data delivery and analysis

Users Rave

"I must say old timers in modeling are dumbfounded by the power of your program." Charles Koehler, Ph.D.

"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 Adams, Dean & Company, a strategy consulting firm in the U.S.

"The 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 Viale, Doctoral thesis student Laboratoire de Physiologie, Faculte de Medecine, France

"KnowledgeMiner is the most advanced implementation of the GMDH approach today. It uses the inductive method, which is different from deductive techniques used commonly for modeling on principle. Recently many important results were demonstrated using this software tool. They show its advantages over other well-known software." Prof. Alexey G. Ivakhnenko, author of the GMDH approach.

"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." Alexis Pobedonostzeff, Pfizer Inc. Director, Health Care Issues Analysis & Management

KnowledgeMiner Software is a privately held software publisher based in both in Germany and the US. KnowledgeMiner Software is a worldwide provider of predictive analytics, data mining and data modeling software and solutions since 1996. KnowledgeMiner is a trademark of KnowledgeMiner Software. For additional information, please visit them online. Microsoft Word is a registered trademark of Microsoft Inc.

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