FM: a fast map algorithm for data clustering

Abstract

We propose a fast map (FM) algorithm, which has significant advantages for knowledge discovery applications due to its low running time and hierarchical clustering capability compared with similar algorithms. The FM algorithm is presented in detail and the effect of a spread factor is investigated. The spread factor can control the growth of network structure (number of nodes and connections), and it is also presented as a method of achieving hierarchical clustering of a data set. Only a small network is created at the beginning with a low spread factor, further analysis is conducted on selected sections of the data, which have smaller volume. Therefore, this method facilitates the analysis of even very large data sets.