Clustering　has　long　been　an　enduring　and　promising　task　in　machine　learning.　However,　developed　one-side　clustering　is　still　insufficient　to　explore　the　context　of　data,　such　as　texts　and　genes.　Hence,　developing　two-way　clustering　has　drawn　more　attention　in　recent　years,　which　tends　to　cluster　samples　and　features　simultaneously.　This　paper　proposes　a　sparse　neighbor　constrained　co-clustering　via　category　consistency　learning,　for　alleviating　the　misclassification　of　close　points.　Following　an　additional　observation,　samples　often　fall　into　the　same　category　as　their　neighbors,　as　do　features.　Accordingly,　the　co-clustering　problem　is　formulated　as　nonnegative　matrix　tri-factorization　appended　dual　regularizers,　considering　coherence　between　data　affinity　and　label　assignment.　Then,　a　multiplicative　alternating　scheme　is　raised　for　objective　optimization,　whose　convergence　and　correctness　are　theoretically　guaranteed.　Furthermore,　the　proposed　approach　is　validated　on　six　datasets　using　three　evaluation　metrics,　whose　parameter　sensitivity　is　analyzed　as　well.　Finally,　comprehensive　experiments　show　that　our　algorithm　is　competitive　against　existing　ones.　(C)　2020　Elsevier　B.V.　All　rights　reserved.