Peculiarity-oriented　mining　(POM)　is　a　new　data　mining　method　consisting　of　peculiar　data　identification　and　peculiar　data　analysis.　Peculiarity　factor　(PF)　and　local　peculiarity　factor　(LIT)　are　important　concepts　employed　to　describe　the　peculiarity　of　points　in　the　identification　step.　One　can　study　the　notions　at　both　attribute　and　record　levels.　In　this　paper,　a　new　record　LPF　called　distance　based　record　LPF　(D-record　LPF)　is　proposed,　which　is　defined　as　the　sum　of　distances　between　a　point　and　its　nearest　neighbors.　It　is　proved　mathematically　that　D-record　LPF　can　characterize　accurately　the　probability　density　function　of　a　continuous　m-dimensional　distribution.　This　provides　a　theoretical　basis　for　some　existing　distance　based　anomaly　detection　techniques.　More　important,　it　also　provides　an　effective　method　for　describing　the　class-conditional　probabilities　in　the　Bayesian　classifier.　The　result　enables　us　to　apply　peculiarity　analysis　for　classification　problems.　A　novel　algorithm　called　LPF-Bayes　classifier　and　its　kernelized　implementation　are　presented,　which　have　some　connection　to　the　Bayesian　classifier.　Experimental　results　on　several　benchmark　data　sets　demonstrate　that　the　proposed　classifiers　are　effective.