By Kermit E. Brown
Electrical Submersible Centrifugal PumpsHydraulic Pumping - Piston TypeJet PumpingOther equipment of man-made LiftPlanning and evaluating synthetic elevate structures
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If ws(x, y) > 0 and a strong dissimilarity is detected we do not state that x and y are overall similar or not, and s(x, y) = 0. If ws(x, y) < 0 and, a strong dissimilarity is observed, then x and y are certainly not overall similar and s(x, y) = −1. Finally, if ws(x, y) = 0 is observed conjointly with a strong dissimilarity, we will conclude that x and y are indeed not overall similar and s(x, y) is put to −1. Property: The overall dissimilarity represents the negation of the overall similarity: d = −s.
Finally, experiment results are provided for validating our proposal over some known data sets. Keywords: Distributed clustering, Graph, Privacy-preserving data. 1 Introduction Data mining is the process of extracting patterns from data and transforming it into knowledge. It deals with a large field of research with some known problems which are related to data: their huge quantity, high dimensionality and complex structure.
Edges valued by −1 are not represented and the zero-valued one is dashed. As a Condorcet similarity graph is always reflexive, we do not represent the loops on the nodes. 00 a c d b Fig. 2. Encoding of the attributes (left) and bipolar-valued similarity relation with associated Condorcet similarity graph (right) 3 Definition of the Clusters Ideally, a cluster would have all the objects inside it similar to each other and dissimilar from the rest. In graph theory this may be modeled by a maximal clique, however, we would also need the maximal clique to be totally disconnected from the rest of the graph, which on real data will very rarely be the case.