Cluster Metric Sensitivity to Irrelevant Features

arXiv:2402.12008v1 Announce Type: new
Abstract: Clustering algorithms are used extensively in data analysis for data exploration and discovery. Technological advancements lead to continually growth of data in terms of volume, dimensionality and complexity. This provides great opportunities in data analytics as the data can be interrogated for many different purposes. This however leads challenges, such as identification of relevant features for a given task. In supervised tasks, one can utilise a number of methods to optimise the input features for …

abstract algorithms analysis analytics arxiv challenges cluster clustering complexity cs.ai cs.lg data data analysis data analytics data exploration dimensionality discovery exploration features growth leads opportunities sensitivity stat.ml terms type