5 research outputs found
Sequence spaces M ( Ï• ) and N ( Ï• ) with application in clustering
Abstract Distance measures play a central role in evolving the clustering technique. Due to the rich mathematical background and natural implementation of l p distance measures, researchers were motivated to use them in almost every clustering process. Beside l p distance measures, there exist several distance measures. Sargent introduced a special type of distance measures m ( Ï• ) and n ( Ï• ) which is closely related to l p . In this paper, we generalized the Sargent sequence spaces through introduction of M ( Ï• ) and N ( Ï• ) 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• ) -distance measure (induced by the Sargent sequence space M ( Ï• ) ) in the k-means clustering algorithm. The clustering result established the efficacy of replacing the Euclidean distance measure by the M ( Ï• ) -distance measure in the k-means algorithm