Volume 13, Issue 5
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Volume 13, Issue 5
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| Sept/Oct 1999 | |
| Theme: Data Mining |
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To Volume 13, Issue 4
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To Volume 13, Issue 6
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Features
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| Rediscovering What You Do: A Data Mining and Rule Discovery Approach to Business Forecasting with Adaptive, Genetically-Tuned Fuzzy System Models Earl Cox explores ways to make your models more responsive to change in demographics and the economy. |
| AI@Work Automated Cardiac Monitoring using Holographic/Quantum Neural Technology, Threatened Fauna Adviser: Tasmania Forestry, Gallagher Integration Includes User Interface to Rule Definition, CLONTECH Uses TextAnalyst to Process Large Volumes of Scientific Texts, Interesting Rules Describe Data. |
| Distributed Task Coordination with Truth Maintenance Systems: AI in Air Liner Design Arkady Epshteyn and Richard H. Stottler cover an AI tool which aids the management and coordination of complex tasks. |
| Data Mining, Modeling, Simulation and Genetic Algorithms in the Chemical Process Industries Paul Van Buskirk describes applications of modeling across numerous arenas. |
| Data Mining with Self-Organizing Maps: Best Practices in Finance, Economics, and Modeling Guido Deboeck uses lessons gleaned froma variety of applications to describe the best processes including analysis, clustering, visualization, and the use of unsupervised neural networks with competitive learning. |
| Investigating Jitter Methods: Measuring What Matters Will Dwinnell illustrates how these jitters won't make you nervous when you buy your next house and other real world examples. |
| Regulars | ||
| Editorial | ||
| Secret Agent Man - Keeping Secrets in the Age of Multi-Agent Collaboration | by Don Barker | |
| Intelligence Files - Oracle "mines" Thinking Machines to Acquire Darwin | by David Blanchard | |
| AI and the Net - Wear the Web | by Mary Kroening | |
| The Book Zone - Data Preparation for Data Mining and Tracking Kalman Filtering Made Easy | by Will Dwinnell | |
| Product Updates ---------------------------> | 15 late breaking product announcements from around the world in the fields of: | |
| Business Rules | Data Mining | |
| Forecasting | Intelligent Agents | |
| Languages | Modeling and Simulation | |
| Neural Networks | Announcements | |
| Product Service Guide - Provides access to information on an entire category of products | ||
| PC AI Blackboard - AI advertisers bulletin board | ||
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Advertiser List for 13.5
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| AAAI | Frontier GlobalCenter | Search Software America |
| Amzi! Inc | Knowledge Brokers Inc | StatSoft |
| AI Developers | Logic Programming Associates | System Dynamics International Inc |
| Angoss | Lumina Decision Systems | The Haley Enterprise Inc |
| AND Corporation | Megaputer Intelligence | Ward Systems Group Inc |
| Applied Logic Systems | Metus Systems | WizSoft Inc |
| ATTAR Software USA | NeuroDimension Inc | |
| BioComp | PC AI | |
| Blaze Corporation | QMC | |
| BotSpot | Production Systems Technology | |
| California Scientific Software | Prolog Development Center | |
| DCI | Salford Systems | |
| DTSoftware . | Soft Warehouse Inc | |
| Franz, Inc | Sonalysts Inc |
| Many of you have or are aware of membership cards provided by larger grocery store chains. These cards, while providing the holder with modest cost savings, allow the merchant to continuoulsy amass information about their clients' shopping habits. What these grocers have discovered is that the conventional use of "averages" and "totals" hides valuable information. As it turns out, their most important customers are not average and don't have average shopping habits. For example, although a certain wine doesn't rate high in total sales, it may be very popular among the highest spending shoppers. Even though this particular brand may be lost in the "averages" it brings shoppers into the store. If the wine isn't available, the clients might move to a competitor's store. It is knowledge, such as this brand loyalty, that the merchant needs to discover and maintain and this is also where data mining can shine. With its ability to collect tremendous amounts of information data, the computer also hides important information. In this issue we look at techniques and tools that help find and identify this special knowledge. | |
| Will Dwinnell illustrates additional examples of data in "Investigating Jitter Methods: Measuring What Matters." Along with these examples, Will demonstrates how to determine which inputs are important as well as measuring the importance of the inputs and their effect on the output. In his latest article, Earl Cox examines methods for making business models more responsive to fluctuation in demographics and the economy. Earl uses a number of different technologies, such as fuzzy logic and genetic algorithms in his data mining. | |
| Based on his research of numerous data mining applications, Guido Deboeck examines the best practices of applying data mining to finance, economics or marketing applications. He looks at self-organizing maps, clustering, visualization, and neural networks. | |
| These are just a few of the examples presented in this issue. We hope you enjoy it. | |
| Terry Hengl |
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