A 2E4-time algorithm for MAX-CUT by Kulikov A. S. PDF

By Kulikov A. S.

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9 Review Data exploration and modeling are critical components of operational, tactical, and strategic planning. However, in this age of distributed computing, data, and knowledge, building models solely from subject matter experts and a conceptual understanding of the problem is a long, protracted, and error-prone process. Coupling knowledge discovery with advanced forms of adaptive fuzzy models can produce highly robust systems that can detect and repair their own logic. After reading this chapter you should have an appreciation for the following.

They also created fields to indicate the type of project, its perceived level of complexity, and its degree of dependence on advanced technology. A supervised data mining analysis discovered a clear relationship between low user satisfaction (as well as project failure) and a set of important project characteristics (surprisingly, the initial budget for the project rather than the final cost was an important indicator of long-term project success). Unsupervised Knowledge Discovery Unsupervised knowledge discovery starts out with a tabula rasa, a clean slate.

24 ■ Chapter 1 Foundations and Ideas decision making model. The ability of fuzzy models to easily incorporate evidence from several expert sources (as well as assign degrees of credibility to each source) makes them an ideal vehicle for building shared decision models in the distributed data warehouse and data mart environment. 11 shows a product pricing model and its various distributed sources of information. 11, the bold parts of the business rules represent fuzzy sets. These fuzzy sets are combined under the methods of fuzzy composition.

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A 2E4-time algorithm for MAX-CUT by Kulikov A. S.

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