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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.

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*To guarantee fair access to the shared medium, a station that has just transmitted a packet and has another packet ready for transmission must perform the backoff procedure before initiating the second transmission.

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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.

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Because the COOP DDBE provides complete distribution transparency (as discussed in 2), the SQL commands sent by the client do not need to include any table fragmentation, location, or replication details. The only requirement for the client SQL is that it be written using the accepted syntax (a subset of ANSI SQL excluding DDL, DML, and overly complex queries) and be unambiguous and correct with respect to that syntax. The syntax requirements for the global SQL are a subset of the requirements we would expect to see for any DBE supporting ANSI SQL. Because the syntax must be validated for every query before it can be processed by the DDBE, we perform syntax validations in the subsystem responsible for providing the query service (the DQSP). Only syntactically correct queries need to be checked for ambiguity (because queries with syntax errors will not be processed).

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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