  In a real-world data set there is always the possibility, rather high in our
opinion, that different features may have different degrees of relevance. Most
machine learning algorithms deal with this fact by either selecting or
deselecting features in the data preprocessing phase. However, we maintain that
even among relevant features there may be different degrees of relevance, and
this should be taken into account during the clustering process. With over 50
years of history, K-Means is arguably the most popular partitional clustering
algorithm there is. The first K-Means based clustering algorithm to compute
feature weights was designed just over 30 years ago. Various such algorithms
have been designed since but there has not been, to our knowledge, a survey
integrating empirical evidence of cluster recovery ability, common flaws, and
possible directions for future research. This paper elaborates on the concept
of feature weighting and addresses these issues by critically analysing some of
the most popular, or innovative, feature weighting mechanisms based in K-Means.
