With　the　development　of　abnormal　behavior　analysis　technology,　measuring　the　similarity　of　abnormal　behavior　has　become　a　core　part　of　abnormal　behavior　detection.　However,　there　are　general　problems　of　central　selection　distortion　and　slow　iterative　convergence　with　existing　clustering-based　analysis　algorithms.　Therefore,　this　paper　proposes　an　improved　clustering-based　abnormal　behavior　analysis　algorithm　by　using　K-means.　Firstly,　an　abnormal　behavior　set　is　constructed　for　each　user　from　his　or　her　behavioral　data.　A　weight　calculation　method　for　abnormal　behaviors　and　an　eigenvalue　extraction　method　for　abnormal　behavior　sets　are　proposed　by　using　all　the　behavior　sets.　Secondly,　an　improved　algorithm　is　developed,　in　which　we　calculate　the　tightness　of　all　data　points　and　select　the　initial　cluster　centers　from　the　data　points　with　high　density　and　low　density　to　improve　the　clustering　effect　based　on　the　K-means　clustering　algorithm.　Finally,　clustering　result　of　the　abnormal　behavior　is　got　with　the　input　of　the　eigenvalues　of　the　abnormal　behavior　set.　The　results　show　that,　the　proposed　algorithm　is　superior　to　the　traditional　clustering　algorithm　in　clustering　performance,　and　can　effectively　enhance　the　clustering　effect　of　abnormal　behavior.　©　2018,　Springer　Nature　Switzerland　AG.