Outlier　detection　is　a　useful　technique　in　such　areas　as　fraud　detection,　financial　analysis　and　health　monitoring.　Many　recent　approaches　detect　outliers　according　to　reasonable,　pre-defined　concepts　of　an　outlier　(e.g.,　distance-based,　density-based,　etc.).　However,　the　definition　of　an　outlier　differs　between　users　or　even　datasets.　This　paper　presents　a　solution　to　this　problem　by　including　input　from　the　users.　Our　OBE　(Outlier　By　Example)　system　is　the　first　that　allows　users　to　provide　examples　of　outliers　in　low-dimensional　datasets.　By　incorporating　a　small　number　of　such　examples,　OBE　can　successfully　develop　an　algorithm　by　which　to　identify　further　outliers　based　on　their　outlierness.　Several　algorithmic　challenges　and　engineering　decisions　must　be　addressed　in　building　such　a　system.　We　describe　the　key　design　decisions　and　algorithms　in　this　paper.　In　order　to　interact　with　users　having　different　degrees　of　domain　knowledge,　we　develop　two　detection　schemes:　OBE-Fraction　and　OBE-RF.　Our　experiments　on　both　real　and　synthetic　datasets　demonstrate　that　OBE　can　discover　values　that　a　user　would　consider　outliers.