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Although the GK algorithm possesses a great advantage over the other clustering algorithms as it adapts the clusters according to their real shape, the algorithm is not able to deal with streams of data and limits its scope to batch clustering due mainly to three obstacles These shortcomings have nothing to do with the GK procedure itself but with the attempt to expand all FCM-type clustering algorithms to cases that deal with streaming data a basic characteristic of an evolving-system-type application First, it processes only a xed data set Second, the algorithm assumes that the number of clusters is known in advance Finally, it determines the clusters by an iterative optimization scheme Thus, in practice, the new incoming data cannot be processed by the original algorithm scheme as the stream of incoming data changes over time.

Proceedings of the Electrical Overstress/Electrostatic Discharge (EOS/ESD) Symposium, 1990 p 119 30 112 ESD Association ESD STM 52-1998: Standard test method for electrostatic discharge sensitivity testing-machine model (MM) component level, 1998 113 Automotive Electronics Council Machine model electrostatic discharge test AEC-Q100-003-Rev E, 2001 114 JEDEC Solid State Technology Association JEDS22-A115-A: Electrostatic discharge (ESD) sensitivity testing machine model (MM), 2000 115 ESD Association ESD TR 10-00: Machine model (MM) electrostatic discharge (ESD) investigation reduction of pulse number and delay time, 2000 116 ESD Association ESD STM 531-1999: Standard test method for electrostatic discharge sensitivity testing-charged device model (CDM) component level, 1999..

The idea of an alternative recursive clustering algorithm that is similar to the concept of the GK algorithm in the sense of using clusters with general data structure is presented in the following Our solution is inspired by two of the most common recursive clustering algorithms the k-nearest neighbor (k-NN) [24] and the linear vector quantization (LVQ) [25] algorithms We do not consider the proposed algorithm an evolving version of the GK algorithm but a rather recursive complement that uses the same Mahalanobis distance like metrics and the norm-inducing matrix while avoiding the aforementioned drawbacks One simpli ed solution could be formulated according to the direct implementation of the k-NN approach Originally, we will be assuming that the GK has been applied to identify an initial set of c clusters of the previously collected data.

ve-number-summary for the churn = true customers indicates higher day minute usage than for the churn = false customers, supporting the observation from Figure 42 Is this difference signi cant A two-sample t-test is carried out, the null hypothesis being that there is no difference in true mean day minute usage between churners and nonchurners The results are shown in Table 49 The resulting t-statistic is 968, with a p-value rounding to zero, representing strong signi cance That is, the null hypothesis that there is no difference in true mean day minute usage between churners and nonchurners is strongly rejected A word of caution is in order here about carrying out inference in data mining problems, or indeed in any problem where the sample size is very large Most statistical tests become very sensitive at very large sample sizes, rejecting the null hypothesis for tiny effects.

Each of those clusters is de ned by its center vi and fuzzy covariance matrix Fi This assumption is realistic given the fact that in almost all evolving applications some initial knowledge about the data being processed is available We assume that the boundary of each cluster is de ned by the cluster radius, which is calculated from the membership degrees (125) The radius ri of the ith cluster is equal to the maximal distance between the cluster center vi and the points belonging to this cluster with membership degree larger than or equal to a given threshold membership degree uh : ri max vi xj Ai 12:8.

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led psychologists to relegate knowledge about women to the margins and to regard questions about gender as having little import or signi cance.

Figure 5-7. The special requests screen assists reservationists in meeting a guest s request for various room accommodation.

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Figure 10.10 Typical differential delay across a voice channel, FDM equipment back-to-back. Backto-back means that the transmit and receive portions of the equipment are placed on a test bench and appropriately interconnected.