Unsupervised　feature　selection　is　an　important　problem,　especially　for　high-dimensional　data.　However,　until　now,　it　has　been　scarcely　studied　and　the　existing　algorithms　cannot　provide　satisfying　performance.　Thus,　in　this　paper,　we　propose　a　new　unsupervised　feature　selection　algorithm　using　similarity-based　feature　clustering,　Feature　Selection-based　Feature　Clustering　(FSFC).　FSFC　removes　redundant　features　according　to　the　results　of　feature　clustering　based　on　feature　similarity.　First,　it　clusters　the　features　according　to　their　similarity.　A　new　feature　clustering　algorithm　is　proposed,　which　overcomes　the　shortcomings　of　K-means.　Second,　it　selects　a　representative　feature　from　each　cluster,　which　contains　most　interesting　information　of　features　in　the　cluster.　The　efficiency　and　effectiveness　of　FSFC　are　tested　upon　real-world　data　sets　and　compared　with　two　representative　unsupervised　feature　selection　algorithms,　Feature　Selection　Using　Similarity　(FSUS)　and　Multi-Cluster-based　Feature　Selection　(MCFS)　in　terms　of　runtime,　feature　compression　ratio,　and　the　clustering　results　of　K-means.　The　results　show　that　FSFC　can　not　only　reduce　the　feature　space　in　less　time,　but　also　significantly　improve　the　clustering　performance　of　K-means.　©　2018　Wiley　Periodicals,　Inc.