Peculiarity-oriented　mining　is　a　data　mining　method　consisting　of　peculiar　data　identification　and　peculiar　data　analysis.　Peculiarity　factor　and　local　peculiarity　factor　are　important　concepts　employed　to　describe　the　peculiarity　of　a　data　point　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.　The　authors　prove　that　D-record　LPF　can　characterize　the　probability　density　of　a　continuous　m-dimensional　distribution　accurately.　This　provides　a　theoretical　basis　for　some　existing　distance-based　anomaly　detection　techniques.　More　importantly,　it　also　provides　an　effective　method　for　describing　the　class-conditional　probabilities　in　a　Bayesian　classifier.　The　result　enables　us　to　apply　D-record　LPF　to　solve　classification　problems.　A　novel　algorithm　called　LPF-Bayes　classifier　and　its　kernelized　implementation　are　proposed,　which　have　some　connection　to　the　Bayesian　classifier.　Experimental　results　on　several　benchmark　datasets　demonstrate　that　the　proposed　classifiers　are　competitive　to　some　excellent　classifiers　such　as　AdaBoost,　support　vector　machines　and　kernel　Fisher　discriminant.