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KnowledgeMiner Software - Creators of (yX) for Excel and KnowledgeMiner® Classic

What is KnowledgeMiner Software?
For more than 15 years KnowledgeMiner Software creates knowledge mining tools that enable anyone to use data modeling to quickly visualize new possibilities. It has been doing research and consulting in several fields. One of these fields it has been active in more intensely is about modeling and prediction of toxicological and ecotoxicological hazards and risks of chemical compounds also for regulatory purposes with the goal to substitute and minimize still widely used animal tests by computer models. Another project which has started recently and which exclusively and extensively uses KnowledgeMiner Software tools is focused around climate change related problems, modeling, and prediction. Sales and demand predictions, macro- and micro-economic modeling problems, budget and resource planning, energy consumption analysis and prediction, medical diagnosis of diseases, traffic prediction, wastewater reuse problems are further examples where KnowledgeMiner tools have been applied to.

Why choose KnowlegeMiner?
The main advantages of the inductive KnowledgeMiner approach are:

  • Only minimal, uncertain a priori information about the system is required. That means, even if you have no experience in modeling, data analysis or designing a neural network you will be able to model, analyze and predict complex relationships of nearly any kind of system.
  • A very fast and effective learning process for a personal computer. That means you can solve problems on your desktop in a reasonable time which you may have never thought possible.
  • Modeling short and noisy data samples. That means, you can deal with a problem as is and don't have to construct artificial conditions for your modeling method to get it work.
  • Output of an optimally complex model. Generally you can be sure to get a model at the end of the automated modeling process which can be expected not to be overfitted. Overfitted models are not able to predict inherent relationships between variables.
  • Output of an analytical model as a transparent explanation component. That means, you can evaluate the analytical model to explain the obtained results immediately after modeling.
About KnowledgeMiner® (yX)                  Information is not knowledge. - Albert Einstein

What is KnowledgeMiner (yX) for Excel?

KnowledgeMiner (yX) for Excel is a knowledge mining tool that works with data stored in Microsoft Excel for building predictive and descriptive models from this data autonomously and easily. It supports all major releases of Microsoft Excel, 2011, 2008 and 2004. The modeling engine of KnowledgeMiner (yX) for Excel implements unique modeling technologies which are built on the principles of self-organization: Learning from noisy data an unknown relationship between output and input of any given system in an evolutionary way from a very simple model to an optimal complex one which generalizes well.

KnowledgeMiner (yX) for Excel implements a completely redesigned and redeveloped modeling engine called (yX). It is based on the modeling technologies also found in our classic KnowledgeMiner 6.0 software, which has been successfully used by our customers for more than 10 years.

KnowledgeMiner (yX) in brief

  • Unique self-organizing modeling technologies implemented in the cross-platform (yX) modeling engine (Mac, Windows, Linux) to make modeling most objective and easy-to-use. More ...
  • An analytical model that describes the data is produced and availabe to the user for implementing the model in Excel, for example.
  • Analytical model implemented in a new Excel worksheet by (yX) for Excel.

  • High-dimensional modeling which scales to data sets of very small (< 20) to large number of samples and from small to large number of potential inputs (<= 50,000). Not any prior variables selection is necessary! Go self-organize models from high-dimensional data directly and support our climate change modeling and prediction project.
  • Original approaches to noise immunity and evaluation of models derived from data to improve reliability of models and to minimize the risk of getting invalid or chance models from badly sized data sets. More ...
  • 64-bit parallel software. Ultra-fast processing of very computation intensive algorithms by 64-bit, multi-core/multi-processor, and vector processing support. The software automatically scales to the number of CPU-cores found at runtime to take full advantage of current and future multi-core hardware. The basic rule: Modeling speed grows with every core and/or processor available at runtime. More ...
  • High-end professional modeling software at a very competetive price-performance ratio without regular update costs. Profit from future updates free. Prices ...


KnowledgeMiner (yX) for Excel is 64-bit parallel software. It employs a twofold, simultanious parallelism: Vector processing (single instruction - multiple data parallelism (SIMD)) and multi-processing (multiple instruction - multiple data parallelism (MIMD)) to take full advantage of multi-processor and/or multi-core based Macs. Also, it automatically scales to the number of processing elements found at runtime (fig. 1).

2core.png  8core.png

Fig. 1: KnowledgeMiner (yX) for Excel running at optimal speed on dual-core MacBooks and iMacs and/or eight-core Mac Pros.

This means, it runs at optimal speed no matter if the machine it is running on is still a single processor machine or if it is driven by a dual-core or a quad-core CPU or by two quad-core CPUs, for example (fig. 2). This implies that KnowledgeMiner (yX) for Excel will also scale to future many-core hardware not available today. The concluding rule is that (yX) for Excel - unlike the vast majority of any kind of software on the market today - runs faster, almost linear, actually, with increasing number of processing elements and clock speeds (fig. 2).

Fig. 2: KnowledgeMiner (yX) for Excel scales very well to the number of processor cores actually available at runtime.

The new 64-bit parallel modeling engine results in breathtaking performance gains. KnowledgeMiner (yX) for Excel runs more than 600 times faster than the our traditional KnowledgeMiner 6 software as shown in figure 3, according to a recent performance study. This makes KnowledgeMiner users even more productive and gets complex modeling and prediction tasks done in almost no time.

Fig. 3: Average speedup of KnowledgeMiner (yX) for Excel on an 8-core 64-bit Intel-based Mac Pro compared to KnowledgeMiner 5.4 running the same modeling tasks on the same machine.

Starting from version 2.0, KnowledgeMiner (yX) for Excel is high-dimensional modeling software. It is high-dimensional in both data set dimensions: It works on data sets with a large number of samples and on data sets with a large number of potential input variables. Furthermore, the software scales from very small to large data sets in both dimensions so that the user don't have much to care about reliability of the resulting self-organized models. Table 1 shows the ranges which KnowledgeMiner (yX) for Excel is designed to work in as compared to other common data mining methods.

Table 1: Common modeling technologies and their applicability to different data set dimensions.
Sample Size n
number of inputs m
(m < 50)
number of inputs m
(49 < m < 500)
number of inputs m
(499 < m < 50K)
Very Small
(n < 30)
(yX), [R]
(29 < n < 200)
(yX), [R, NN]
(yX), [R]
(199 < n < 10K)
(yX), R, NN
(yX), [R, NN]
(10K < n < 1M)
(yX), R, NN
(yX), [R, NN]
• NN - Neural Networks and other Machine Learning methods.
• R - Statistical regression methods.
• (yX) - Self-organizing, high-dimensional modeling in KnowledgeMiner (yX).
• [.] - Can be applied under certain conditions only.

A major concern for models built from any data-driven modeling technology is model reliability, and the risk of obtaining an invalid model grows fast with increasing number of input variables and with increasing model complexity. Therefore, original research into noise immunity of high-dimensional state space modeling has been performed by KnowledgeMiner Software for many years. This research resulted in highly improved noise immunity algorithms compared to traditional modeling approaches (see fig. 3). These new algorithms has been implemented in KnowledgeMiner (yX) for the first time and they additionally allow at the same time a new model evaluation approach, which helps and supports the user in a powerful way to assess obtained models. This concept is described in more detail in the section "Noise Immunity and Descriptive Power".

Based on our independent, cross-platform (yX) core modeling engine, self-organizing, parallel, high-dimensional modeling with on-the-fly model evaluation provides a unique and original tool which allows also non-experts to build reliable predictive analytical models of complex systems from the desktop with unprecedented power, ease-of-use, and ease-of-applicability likewise.
An example which illustrates the power of KnowledgeMiner (yX) for Excel is recent studies on modeling and prediction of climate change in global and regional scales. One model, for instance, self-organized from 1900 monthly historical air and sea surface temperature data and over 9300 potential input variables (by using a time lag of up to 518 months) is composed of rarely 18 relevant inputs, and it took less than 9 minutes on an 8-core Mac Pro to get this model (fig. 5)! This translates into

  • 36 minutes runtime on a dual-core machine,
  • 72 minutes when running serially, all in 64-bit mode,
  • about 90 minutes serial runtime in 32-bit space, and
  • 3 days and 20 hours if it was running using our classical KnowledgeMiner 6 software!
  • Weeks of work if theory-based and traditional statistical methods are used, and there are many of such models to develop!

Fig. 5: Global temperature prediction model self-organized from a high-dimensional data set.

System Requirements

  • Mac OS X 10.5 (Leopard) or newer
  • Any multi-core Intel-based Mac to run software concurrently (recommended), PPC Macs to run software sequentially, only
  • Microsoft Excel 2011, 2008 or 2004
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