A parameter-free clustering algorithm for missing datasets

arXiv:2404.05363v1 Announce Type: new
Abstract: Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world. Existing clustering algorithms for missing datasets first impute the missing values and then perform clustering. However, both the imputation and clustering processes require input parameters. Too many input parameters inevitably increase the difficulty of obtaining accurate clustering results. Although some studies have shown that decision graphs can replace the input parameters of clustering algorithms, current decision graphs require …

abstract algorithm algorithms arxiv clustering clustering algorithm cs.lg datasets dimensions free however imputation missing values objects parameters processes type values world