5 research outputs found

    Sequence spaces M ( Ï• ) M(Ï•)M(\phi) and N ( Ï• ) N(Ï•)N(\phi) with application in clustering

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    Abstract Distance measures play a central role in evolving the clustering technique. Due to the rich mathematical background and natural implementation of l p lpl_{p} distance measures, researchers were motivated to use them in almost every clustering process. Beside l p lpl_{p} distance measures, there exist several distance measures. Sargent introduced a special type of distance measures m ( Ï• ) m(Ï•)m(\phi) and n ( Ï• ) n(Ï•)n(\phi) which is closely related to l p lpl_{p} . In this paper, we generalized the Sargent sequence spaces through introduction of M ( Ï• ) M(Ï•)M(\phi) and N ( Ï• ) N(Ï•)N(\phi) sequence spaces. Moreover, it is shown that both spaces are BK-spaces, and one is a dual of another. Further, we have clustered the two-moon dataset by using an induced M ( Ï• ) M(Ï•)M(\phi) -distance measure (induced by the Sargent sequence space M ( Ï• ) M(Ï•)M(\phi) ) in the k-means clustering algorithm. The clustering result established the efficacy of replacing the Euclidean distance measure by the M ( Ï• ) M(Ï•)M(\phi) -distance measure in the k-means algorithm
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