This　paper　is　concerned　with　tensor　clustering　with　the　assistance　of　dimensionality　reduction　approaches.　A　class　of　formulation　for　tensor　clustering　is　introduced　based　on　tensor　Tucker　decomposition　models.　In　this　formulation,　an　extra　tensor　mode　is　formed　by　a　collection　of　tensors　of　the　same　dimensions　and　then　used　to　assist　a　Tucker　decomposition　in　order　to　achieve　data　dimensionality　reduction.　We　design　two　types　of　clustering　models　for　the　tensors:　PCA　Tensor　Clustering　model　and　Non-negative　Tensor　Clustering　model,　by　utilizing　different　regularizations.　The　tensor　clustering　can　thus　be　solved　by　the　optimization　method　based　on　the　alternative　coordinate　scheme.　Interestingly,　our　experiments　show　that　the　proposed　models　yield　comparable　or　even　better　performance　compared　to　most　recent　clustering　algorithms　based　on　matrix　factorization.　©　2014　IEEE.