Time　series　clustering　has　been　applied　in　many　scientific　domains　and　has　attracted　much　attention　in　recent　years.　In　this　paper,　a　novel　hypergraph　based　clustering　method　for　time　series　is　proposed＂　combining　multiple　similarity　measures　and　hypergraph　partitioning.　We　firstly　build　the　hypergraph　for　time　series　dataset　using　multiple　similarity　measures,　where　each　time　series　is　represented　by　vertex　and　hyperedge　is　formed　based　on　the　similarity　relation　among　time　series.　Then,　the　vertices　in　the　constructed　hypergraph　are　grouped　by　the　hypergraph　partitioning　method　to　identify　the　clusters　of　time　series.　Two　different　strategies　for　hypergraph　construction　are　presented　in　detail,　resulting　in　two　specific　methods.　Empirical　experiments　of　time　series　clustering　with　the　UCR　archive　are　conducted　for　the　purpose　of　evaluation.　The　results　demonstrate　that　the　proposed　methods　outperform　contrast　clustering　methods　in　most　of　the　tested　datasets　and　also　achieve　better　performance　than　the　network　based　methods　under　consideration,　owe　to　the　combination　of　advantages　of　hypergraph　and　various　similarity　measures.　©　2016　IEEE.