clustering


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clustering

 [klus´ter-ing]
gathering together.
conceptual clustering the process of analyzing, examining relationships in, and organizing theoretically the current knowledge in a field of study in order to add to an existing knowledge base and generate further questions for research.
References in periodicals archive ?
Rodrigues, "Clustering in vehicular Ad Hoc networks: taxonomy, challenges and solutions," Vehicular Communications, vol.
Chong, "A survey of clustering schemes for mobile ad hoc networks," IEEE Communications Surveys & Tutorials, vol.
In order to obtain the optimal clustering results of nonuniform sparse data, in this paper, we use multidimensional diffusion density distribution to obtain the initial data clusters, while the Euclidean distance control factor based on aggregation density sparse degree is put forward to solve the problem that multidimensional data is easy to misjudge.
Randomly select k points as the initial clustering center [[mu].sub.1],[[mu].sub.2],..., [[mu].sub.k] in the data sets [{[x.sub.i]}.sup.N.sub.i=1] and N is the number of samples
All these existing methods for cluster formation are crisp clustering methods where vehicles that are uncertain about their clusters are not properly assigned to the appropriate clusters.
Yoshioka, "Secure Clustering for Building Certificate Mangement Nodes in Ad-Hoc Networks," Proc.
Runkler and Katz [15] introduced two new methods for minimizing the reformulated objective functions of the FCM clustering model by PSO: PSOTh and PSO-Th In order to overcome the shortcomings of FCM, a PSO-based fuzzy clustering algorithm was discussed [16]; this algorithm uses the global search capacity of PSO to overcome the shortcomings of FCM.
Having computed the new similarity matrix, any pair-wise similarity based clustering methods can be used to achieve the consensus clustering.
Then the dataset was divided into north zone (C1), middle zone (C2), and south zone (C3), looking for a relation between the clustering and the existing geology.
(2) The interaction between the users and the clustering result can reveal the intent of their analysis goal.
Cluster analysis is another notation used for data clustering. It is a process of putting similar data into groups.
Select [epsilon] > 0, set the initial clustering centers [V.sup.(0)] = {[v.sub.1], [v.sub.2], ..., [v.sub.c]}, and set the number of iteration l = 1.