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You ve read a bit about entity beans in previous chapters, but in this chapter you ll further explore the vitally important entity bean. You ll read about characteristics of entity beans and the entity bean interface, in addition to learning about container-managed and bean-managed persistence. You ll also learn about the life cycle of an entity bean, in addition to examining some code that implements an entity bean. Let s start by learning about the characteristics of entity beans and their uses.

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There are different mechanisms affecting the dielectric behavior of cellular food systems. These mechanisms are divided into two classes: resonance and relaxation processes. Resonance phenomena happen when the applied electric eld has a frequency that matches the natural oscillation frequency of the material. It includes electronic polarization and atomic polarization, which are produced at the highest microwave frequencies and above them (Fig. 25.3). Electronic polarization results from the displacement of the electronic cloud with respect to the nucleus, whether in single atoms or in molecules (Grimnes and Gr ttem-Martinsen 2008). The effects of this particular polarization can be observed in the visible part of the spectrum,

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properly through the use of empirical data sets for validation purposes Most classi cation researchers have turned to the use of computer-generated data sets for establishing clustering validity Simulation or Monte Carlo experiments allow the researcher to know the exact cluster structure underlying the data This strategy has the advantage that the true clustering is known The extent to which any given clustering algorithm has recovered this structure can be determined Because of the use of arti cially generated data sets, simulation results can be based on hundreds or thousands of data sets Thus, sample size is not an issue There is a serious weakness in the use of simulation methods In every case, such results are limited on the basis of generalizability That is, the Monte Carlo results may be valid only for the types of cluster structures and distributions that were present in the generated data sets.

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Thus, the effectiveness of the algorithms may not extend to other data structures that are possible in applied analyses Thus, it is important to establish replicability of simulation results from differing studies It is especially valuable when different researchers achieve similar results using different strategies for data generation and evaluation Such replications offer investigators more con dence in the selection of methods for applied analyses In terms of results on the recovery of underlying cluster structure, agglomerative hierarchical algorithms have been the most extensively studied Three reviews of Monte Carlo clustering studies covering various time frames were published by Milligan (1981a), Milligan and Cooper (1987), and Milligan (1996) The validation studies have examined a number of factors that might affect recovery of the underlying clusters Many studies have included an error-free data condition.

The clustering present in the error-free data typically was so distinct that almost any method should have been able to perform well with this sort of simple and obvious data structure Clustering methods that fail with errorfree data would not be suitable for most applied research settings A second factor examined has been the introduction of some sort of error, either on the underlying variables or directly to the similarity measures This condition has the capability of being tuned to a gradient of increasing noise An effective clustering method should be capable of nding clusters that have been hidden by moderate amounts of error in the data A different sort of error involves the introduction of outlying data points to a core set of elements that de nes a suitable cluster structure Unusual observations are not unusual in behavioral research.

The authors will now proceed to present their own (obviously superior) valuation models. For those readers who are mathematically inclined

A clustering method used for applied analyses should have some insensitivity to the presence of such data points..

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