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Text File | 1999-03-22 | 2.7 KB | 72 lines | [TEXT/ttxt] |
- KnowledgeMiner History
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- March/23/1999 - version 3.0
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- • New modeling method: Self-organizing Fuzzy Rule Induction (Fuzzy-GMDH)
- • available for the first time on any personal computer
- • combines modeling of fuzzy objects with natural language-like interpretational power of the generated models
- • larger and now customizable data sheet (up to 10,000 rows/ 200 columns)
- • improved GMDH by information matrix optimization
- • improved and updated Help menu (AppleGuide, Balloons)
- • updated and extended examples collection
- • fixed bug that may occur when reading ASCII text files
- • scaled into Copper, Silver and Gold editions
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- June/28/1998 - version 2.2.3
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- • first PPC only version
- • improved Analog Complexing method
- • several bugs are fixed making the program more stable under low memory conditions
- • redesigned "Modeling" and "Window" menus
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- February/4/1998 - version 2.2.2
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- • fixed bug that may occur when reading large files
- • new features will not be supported for the 68k based version beginning from this version
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- December/15/1997 - version 2.2.1
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- • 32k ASCII text import limit removed
- • fixed bug that causes a crash when calculating a large number of formulas in spreadsheet
- • fixed bug on PowerMac that does a wrong cell assignment when verifying a spreadsheet input by mouse click
- • improved and updated the text files and apple guide
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- October/24/1997 - version 2.1
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- • tutorial updated - new context check features
- • removed bug that allowed opening two documents at once on PowerMac
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- October/1/1997 - version 2.0
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- • optimized for PPC and 68kFPU based Macs
- • PPC native app runs 8-10 times faster than 68k based version. Now it is possible to mine very large data sets for
- relevant relations or patterns within minutes instead of days.
- • Analog Complexing
- • available for the first time on a personal computer (Mac) as shareware.
- • shows automatically a prediction interval graphically (the calculated possible upper and lower limits) along with
- the most likely prediction of points. Now a prediction consists not only of sharp points but reflects also the
- inherent fuzziness and uncertainty of the objects. Applied to financial time series forecasting, e.g., this view
- focuses on the demand to consider the volatility of assets to get more reliable forecasts and decisions.
- • optimized modeling algorithms to make self-organization of structures and knowledge extraction from data far
- more effective than neural networks or statistics
- • Lite version
- • now has a larger table to work with data
- • two levels to setup modeling
- • a standard and
- • an advanced modeling setup dialog
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- May/1/1997 - initial release
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