Unsupervised Initialization of Archetypal Analysis and Proportional Membership Fuzzy Clustering
Oct 2019
This paper further investigates and compares a method for
fuzzy clustering which retrieves pure individual types from data, known
as the fuzzy clustering with proportional membership (FCPM), with the
FurthestSum Archetypal Analysis algorithm (FS-AA). The Anomalous
Pattern (AP) initialization algorithm, an algorithm that sequentially extracts
clusters one by one in a manner similar to principal component
analysis, is shown to outperform the FurthestSum not only by improving
the convergence of FCPM and AA algorithms but also to be able to
model the number of clusters to extract from data.
A study comparing nine information-theoretic validity indices and the
soft ARI has shown that the soft Normalized Mutual Information max
(NMIsM) and the Adjusted Mutual Information (AMI) indices are more
adequate to access the quality of FCPM and AA partitions than soft internal
validity indices. The experimental study was conducted exploring
a collection of 99 synthetic data sets generated from a proper data generator,
the FCPM-DG, covering various dimensionalities as well as 18
benchmark data sets from machine learning.
