Travel　smart　cards　record　passengers＇　travel　histories,　which　makes　it　possible　to　study　personal　traveling　behaviors　and　passengers＇　mobility　patterns.　The　existing　researches　on　smart　card　data　pay　less　attention　to　those　who　beg,　steal　or　busk　during　traveling,　and　they　are　called　irregular　passengers　in　this　paper.　Moreover,　the　group　consisted　of　irregular　passengers　is　called　irregular　travel　group.　In　this　paper,　we　propose　an　approach　to　recognize　irregular　travel　groups　based　on　cascade　clustering.　Firstly,　passengers＇　travel　sequences　of　active　state　in　hours　are　extracted　as　representations　of　travel　patterns　according　to　the　records　of　smart　cards　in　a　continuous　time　period.　The　sequences　are　clustered　with　the　K-means　algorithm　to　detect　irregular　passengers.　Then,　travel　similarities　between　the　derived　irregular　passengers　are　measured　and　irregular　travel　groups　are　recognized　based　on　DBSCAN　(Density-Based　Spatial　Clustering　of　Applications　with　Noise)　algorithm.　The　proposed　method　is　applied　to　real　Beijing　subway　smart　card　data,　and　the　results　are　validated　with　the　data　derived　from　SINA　Micro-blogs.　Experimental　results　show　that　the　detected　irregular　passengers＇　movement　areas　are　consistent　with　the　ground　truth.