Self Organizing Features Maps are used for a variety of tasks in visualization and clustering, acting to transform data from a highdimensional original feature space to a (usually) two-dimensional grid. SOFMs use a similarity metric in the input space, and this composes individual feature differences in a way that is not always desirable. This paper introduces the concept of a Pareto SOFM, which partitions features into groups, defines separate metrics in each partition, and retrieves a set of prototypes that trade off matches in different partitions. It is suitable for a wide range of exploratory tasks, including visualization and clustering. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Hunter, A., & Kennedy, R. L. (2002). A pareto Self-Organizing Map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 987–992). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_160
Mendeley helps you to discover research relevant for your work.