As　a　method　built　upon　spectral　graph　theory,　spectral　clustering　has　the　advantages　of　processing　data　with　any　spatial　shapes　and　converging　on　global　optimal　solutions.　But　it　suffers　from　the　defects　that　the　clustering　result　is　quite　sensitive　to　its　parameters　and　the　number　of　clusters　must　be　prespecified.　In　this　paper,　a　novel　approach　which　integrates　the　grey　relational　analysis　based　on　difference　information　theory　and　a　self-tuning　method　with　spectral　clustering　is　proposed.　The　similarities　between　data　points　are　described　by　the　balanced　closeness　degrees　of　their　attribute　sequences.　A　cost　function　is　optimized　to　recognize　the　number　of　clusters　automatically.　So,　the　impact　of　the　parameters　can　be　eliminated　and　the　performance　can　be　improved.　The　experimental　results　proved　the　effectiveness　of　the　new　algorithm.　©　2010　IEEE.