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

A survey on feature weighting based K-Means algorithms