Although　kernel　fuzzy　clustering　can　handle　non-spherical　clusters　by　mapping　data　to　a　more　separable　feature　space,　its　performance　is　highly　determined　by　the　setting　of　kernels.　So,　the　multiple　kernel　fuzzy　clustering　(MKFC)　is　proposed　to　obtain　the　flexibility　in　designing　an　optimal　kernel　from　a　large　set　of　candidates.　In　MKFC,　many　predefined　general　kernels　like　Gaussian　and　polynomial　ones　are　linearly　aggregated　and　the　weights　of　kernels　are　adjusted　automatically.　However,　the　performance　of　MKFC　is　greatly　hampered　by　two　noticeable　problems.　First,　MKFC　only　uses　predefined　general　kernels　and　pays　less　attention　to　the　inherent　structure　of　specific　data.　This　leads　to　the　trouble　of　selecting　proper　base　kernels　for　different　data.　The　second　problem　is　the　ignorance　of　correlations　between　kernels　in　MKFC.　It　results　in　redundant　kernels　being　used　to　define　the　feature　space.　This　paper　solves　the　two　problems　simultaneously　by　introducing　a　new　MKFC　model.　Based　on　unsupervised　random　forests　(RFs),　some　data-dependent　kernels　are　generated　and　combined　with　others　to　build　a　more　representative　feature　space.　The　correlations　between　kernels　are　also　calculated　and　inserted　into　the　objective　function　of　fuzzy　clustering　as　a　matrix-induced　regularization　to　encourage　the　diversity　in　kernels.　We　name　the　new　model　as　MKFC　with　unsupervised　RFs　kernel　and　matrix-induced　regularization.　The　optimization　algorithm　for　the　new　model　is　derived,　and　the　experiments　on　benchmark　datasets　demonstrate　its　superiority　over　other　MKFC　approaches.