KnowledgeMiner (yX) for Excel 2.0
KnowledgeMiner (yX) for Excel 2.0 Ranking & Summary
KnowledgeMiner (yX) for Excel 2.0 description
KnowledgeMiner (yX) for Excel 2.0 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. 2 data modeling applications, (yX) for Excel and KnowledgeMiner 6.0 both in one download.
yX has 3 main differences from KnowledgeMiner.
- (yX) works in Microsoft Excel and can export models back to Excel.
- (yX) contains a subset of the features in KnowledgeMiner.
- (yX) Is optimized and parallelized to operate more than 600x faster then KnowledgeMiner.
These are both based on the same revolutionary data mining algorithms - self-organizing networks of active neurons (gmdh), self-organizing fuzzy rule induction, analog complexing and others that learn and model data. KnowledgeMiner and (yX) can be used for different data mining tasks like classification, modeling, time series forecasting, sequential patterns and clustering. KnowledgeMiner and (yX) are useful and essential for research in all fields of knowledge. Includes examples
Major Features:
- Model selection procedure during model self-organization which may lead to more accurate models;
- Automatic generation of per-sample model and prediction uncertainty now also for self-organized regression models;
- Automatic generation of graphs for models in Excel including a prediction uncertainty plot;
- Significant performance improvement for building regression models on data sets with large number of samples;
- Essential memory requirement reduction for self-organizing regression modeling;
Enhancements:
- High-dimensional modeling which scales to data sets of very small (< 20) to large number of samples (<= 1M) and from small to large number of potential inputs (<= 50,000). Not any prior variables selection is necessary!
- Original tools 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 ...
- Climate change modeling and prediction project added. This project is possible now since it requires building reliable models from data sets which are high-dimensional in input variables space;
- Tutorial and documentation;
Requirements:
- Mac OS X 10.5 or newer
- Microsoft Excel 2004 or 2008
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