Download Adaptive Business Intelligence by Zbigniew Michalewicz PDF

By Zbigniew Michalewicz

Adaptive company intelligence platforms mix prediction and optimization thoughts to aid determination makers in complicated, swiftly altering environments. those platforms deal with basic questions: what's prone to occur sooner or later? what's the top plan of action? Adaptive enterprise Intelligence explores parts of information mining, predictive modeling, forecasting, optimization, and suppleness. The publication explains the appliance of diverse prediction and optimization recommendations, and indicates how those strategies can be utilized to boost adaptive structures. insurance contains linear regression, time-series forecasting, choice bushes and tables, man made neural networks, genetic programming, fuzzy platforms, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.

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Of course, our inventory should also include cars that are in transit to the auction, and before we can make any distribution decision, it would be necessary to check our inventory levels at all 50 auction sites. Otherwise, it would be next to impossible to make a smart decision on where to ship today’s load of cars (this is especially important for large leasing companies, which may have 50,000 or more cars sitting in inventory). 7 Dynamic Market Changes Another issue that makes our job more difficult is the fluctuation in used car prices.

5 The Structure of an Adaptive Business Intelligence System 45 Each of these rules has a weight,15 and the weights of rules can be modified (say, on a weekly basis) to tune the predictions in a changing environment. In other words, the adaptability module can “adapt” to environmental changes by updating these rules, with the update frequency depending on how fast the environment changes. Of course, a really good adaptability module can make its own decision on the update frequency by continuously measuring its own prediction errors.

Finding the relationship between “sale price,” “make,” “model,” and “mileage”). Although knowledge discovery (e. , text mining, discovery of association rules) is oftentimes an important goal of data mining, we are more interested in using the data mining results to build a model (or set of models) for predicting some response (e. , the outcome of a loan application, the probability that a credit card transaction is fraudulent). The reason for this is that predictions are directly applicable to decision making, whereas knowledge discovery is closer to decision support.

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