{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T11:51:38Z","timestamp":1774698698444,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003005","name":"Technische Universiteit Eindhoven","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior.<\/jats:p>","DOI":"10.1007\/s10618-020-00674-z","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T18:03:13Z","timestamp":1580320993000},"page":"1267-1290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4491-4018","authenticated-orcid":false,"given":"Xin","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Pei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wouter","family":"Duivesteijn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mykola","family":"Pechenizkiy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,29]]},"reference":[{"key":"674_CR1","unstructured":"Atluri G, Karpatne A, Kumar V (2017) Spatio-temporal data mining: a survey of problems and methods. arXiv preprint arXiv:1711.04710"},{"issue":"1","key":"674_CR2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1002\/widm.1144","volume":"5","author":"M Atzmueller","year":"2015","unstructured":"Atzmueller M (2015) Subgroup discovery. Wiley Interdiscip Rev Data Min Knowl Discov 5(1):35\u201349","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"674_CR3","doi-asserted-by":"crossref","unstructured":"Becker M, Mewes H, Hotho A, Dimitrov D, Lemmerich F, Strohmaier M (2016) SparkTrails: a MapReduce implementation of HypTrails for comparing hypotheses about human trails, WWW Companion, pp 17\u201318","DOI":"10.1145\/2872518.2889380"},{"key":"674_CR4","doi-asserted-by":"crossref","unstructured":"Bendimerad AA, Plantevit M, Robardet C (2016) Unsupervised exceptional attributed sub-graph mining in urban data. In: 2016 IEEE 16th international conference on data mining (ICDM), IEEE, pp 21\u201330","DOI":"10.1109\/ICDM.2016.0013"},{"issue":"Feb","key":"674_CR5","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(Feb):281\u2013305","journal-title":"J Mach Learn Res"},{"key":"674_CR6","unstructured":"Bergstra JS, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems, pp 2546\u20132554"},{"issue":"2","key":"674_CR7","doi-asserted-by":"publisher","first-page":"7:1","DOI":"10.1145\/1667053.1667056","volume":"57","author":"DM Blei","year":"2010","unstructured":"Blei DM, Griffiths TL, Jordan MI (2010) The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. J ACM 57(2):7:1\u20137:30","journal-title":"J ACM"},{"issue":"3","key":"674_CR8","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):151\u20131558","journal-title":"ACM Comput Surv"},{"issue":"9","key":"674_CR9","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1109\/TVCG.2017.2758362","volume":"24","author":"W Chen","year":"2018","unstructured":"Chen W, Huang Z, Wu F, Zhu M, Guan H, Maciejewski R (2018) Vaud: a visual analysis approach for exploring spatio-temporal urban data. IEEE Trans Visual Comput Gr 24(9):2636\u20132648","journal-title":"IEEE Trans Visual Comput Gr"},{"key":"674_CR10","doi-asserted-by":"crossref","unstructured":"Chierichetti F, Kleinberg JM, Kumar R, Mahdian M, Pandey S (2014) Event detection via communication pattern analysis. In: Proc ICWSM, pp 51\u201360","DOI":"10.1609\/icwsm.v8i1.14536"},{"key":"674_CR11","doi-asserted-by":"crossref","unstructured":"Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1082\u20131090","DOI":"10.1145\/2020408.2020579"},{"key":"674_CR12","doi-asserted-by":"crossref","unstructured":"Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N (2010) Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM international conference on Ubiquitous computing, ACM, pp 119\u2013128","DOI":"10.1145\/1864349.1864380"},{"key":"674_CR13","doi-asserted-by":"crossref","unstructured":"Duivesteijn W, Knobbe A, Feelders A, van Leeuwen M (2010) Subgroup discovery meets Bayesian networks\u2014an exceptional model mining approach. In: 10th international conference on data mining (ICDM), IEEE, pp 158\u2013167","DOI":"10.1109\/ICDM.2010.53"},{"key":"674_CR14","doi-asserted-by":"crossref","unstructured":"Duivesteijn W, Feelders A, Knobbe A (2012) Different slopes for different folks: mining for exceptional regression models with cook\u2019s distance. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 868\u2013876","DOI":"10.1145\/2339530.2339668"},{"issue":"1","key":"674_CR15","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10618-015-0403-4","volume":"30","author":"W Duivesteijn","year":"2016","unstructured":"Duivesteijn W, Feelders AJ, Knobbe A (2016) Exceptional model mining. Data Min Knowl Disc 30(1):47\u201398","journal-title":"Data Min Knowl Disc"},{"key":"674_CR16","doi-asserted-by":"crossref","unstructured":"Giannotti F, Gabrielli L, Pedreschi D, Rinzivillo S (2016) Understanding human mobility with big data. Solving large scale learning tasks. Springer, Challenges and Algorithms, pp 208\u2013220","DOI":"10.1007\/978-3-319-41706-6_10"},{"key":"674_CR17","doi-asserted-by":"crossref","unstructured":"Goldberger J, Gordon S, Greenspan H (2003) An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. In: Proceedings of the ninth IEEE international conference on computer vision\u2013volume 1, IEEE Computer Society, Washington, DC, USA, ICCV \u201903, pp 487\u2013493","DOI":"10.1109\/ICCV.2003.1238387"},{"issue":"7196","key":"674_CR18","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1038\/nature06958","volume":"453","author":"MC Gonzalez","year":"2008","unstructured":"Gonzalez MC, Hidalgo CA, Barabasi AL (2008) Understanding individual human mobility patterns. Nature 453(7196):779\u2013782","journal-title":"Nature"},{"issue":"3","key":"674_CR19","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s10115-010-0356-2","volume":"29","author":"F Herrera","year":"2011","unstructured":"Herrera F, Carmona CJ, Gonz\u00e1lez P, Del Jesus MJ (2011) An overview on subgroup discovery: foundations and applications. Knowl Inf Syst 29(3):495\u2013525","journal-title":"Knowl Inf Syst"},{"key":"674_CR20","doi-asserted-by":"crossref","unstructured":"Hong L, Ahmed A, Gurumurthy S, Smola AJ, Tsioutsiouliklis K (2012) Discovering geographical topics in the twitter stream. In: Proceedings of the 21st international conference on World Wide Web, ACM, pp 769\u2013778","DOI":"10.1145\/2187836.2187940"},{"key":"674_CR21","doi-asserted-by":"crossref","unstructured":"Hooi B, Shah N, Beutel A, G\u00fcnnemann S, Akoglu L, Kumar M, Makhija D, Faloutsos C (2016) Birdnest: Bayesian inference for ratings-fraud detection. In: Proceedings of the SIAM international conference on data mining, SIAM, pp 495\u2013503","DOI":"10.1137\/1.9781611974348.56"},{"key":"674_CR22","doi-asserted-by":"crossref","unstructured":"Jankowiak M, Gomez-Rodriguez M (2017) Uncovering the spatiotemporal patterns of collective social activity. In: Proceedings of the SIAM international conference on data mining, SIAM, pp 822\u2013830","DOI":"10.1137\/1.9781611974973.92"},{"key":"674_CR23","doi-asserted-by":"crossref","unstructured":"Jorge AM, Mendes-Moreira J, de\u00a0Sousa JF, Soares C, Azevedo PJ (2012) Finding interesting contexts for explaining deviations in bus trip duration using distribution rules. In: International symposium on intelligent data analysis, Springer, pp 139\u2013149","DOI":"10.1007\/978-3-642-34156-4_14"},{"issue":"8","key":"674_CR24","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1007\/s10994-016-5598-0","volume":"106","author":"M Kaytoue","year":"2017","unstructured":"Kaytoue M, Plantevit M, Zimmermann A, Bendimerad A, Robardet C (2017) Exceptional contextual subgraph mining. Mach Learn 106(8):1171\u20131211","journal-title":"Mach Learn"},{"issue":"9","key":"674_CR25","doi-asserted-by":"publisher","first-page":"1899","DOI":"10.1080\/13658816.2016.1146956","volume":"30","author":"KS Kim","year":"2016","unstructured":"Kim KS, Kojima I, Ogawa H (2016) Discovery of local topics by using latent spatio-temporal relationships in geo-social mediaa. Int J Geogr Inf Sci 30(9):1899\u20131922","journal-title":"Int J Geogr Inf Sci"},{"issue":"2","key":"674_CR26","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s10994-015-5520-1","volume":"102","author":"K Knauf","year":"2016","unstructured":"Knauf K, Memmert D, Brefeld U (2016) Spatio-temporal convolution kernels. Mach Learn 102(2):247\u2013273","journal-title":"Mach Learn"},{"key":"674_CR27","doi-asserted-by":"crossref","unstructured":"Lane ND, Pengyu L, Zhou L, Zhao F (2014) Connecting personal-scale sensing and networked community behavior to infer human activities. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, ACM, pp 595\u2013606","DOI":"10.1145\/2632048.2636094"},{"key":"674_CR28","doi-asserted-by":"crossref","unstructured":"Lemmerich F, Becker M, Singer P, Helic D, Hotho A, Strohmaier M (2016) Mining subgroups with exceptional transition behavior. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 965\u2013974","DOI":"10.1145\/2939672.2939752"},{"issue":"2","key":"674_CR29","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/s10115-013-0714-y","volume":"42","author":"M Mampaey","year":"2015","unstructured":"Mampaey M, Nijssen S, Feelders A, Konijn R, Knobbe A (2015) Efficient algorithms for finding optimal binary features in numeric and nominal labeled data. Knowl Inf Syst 42(2):465\u2013492","journal-title":"Knowl Inf Syst"},{"key":"674_CR30","doi-asserted-by":"crossref","unstructured":"Meeng M, Duivesteijn W, Knobbe A (2014) ROCsearch \u2014 an ROC guided search strategy for subgroup discovery. In: Proceedings of the 2014 SIAM international conference on data mining, society for industrial and applied mathematics, pp 704\u2013712","DOI":"10.1137\/1.9781611973440.81"},{"key":"674_CR31","unstructured":"Murphy KP (2007) Conjugate bayesian analysis of the gaussian distribution. University of British Columbia, Tech. rep"},{"issue":"1","key":"674_CR32","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10994-013-5399-7","volume":"93","author":"N Piatkowski","year":"2013","unstructured":"Piatkowski N, Lee S, Morik K (2013) Spatio-temporal random fields: compressible representation and distributed estimation. Mach Learn 93(1):115\u2013139","journal-title":"Mach Learn"},{"key":"674_CR33","doi-asserted-by":"crossref","unstructured":"Porteous I, Newman D, Ihler A, Asuncion A, Smyth P, Welling M (2008) Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 569\u2013577","DOI":"10.1145\/1401890.1401960"},{"key":"674_CR34","doi-asserted-by":"crossref","unstructured":"Puolam\u00e4ki K, Kang B, Lijffijt J, De\u00a0Bie T (2016) Interactive visual data exploration with subjective feedback. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, Berlin, pp 214\u2013229","DOI":"10.1007\/978-3-319-46227-1_14"},{"key":"674_CR35","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s10115-017-1077-6","volume":"54","author":"K Shin","year":"2017","unstructured":"Shin K, Eliassi-Rad T, Faloutsos C (2017) Patterns and anomalies in k-cores of real-world graphs with applications. Knowl Inf Syst 54:677\u2013710","journal-title":"Knowl Inf Syst"},{"key":"674_CR36","unstructured":"Shipmon DT, Gurevitch JM, Piselli PM, Edwards ST (2017) Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data. arXiv preprint arXiv:1708.03665"},{"key":"674_CR37","unstructured":"Soch J, Allefeld C (2016) Kullback-Leibler divergence for the normal-gamma distribution. arXiv preprint arXiv:1611.01437"},{"key":"674_CR38","doi-asserted-by":"crossref","unstructured":"Soulet A, Ra\u00efssi C, Plantevit M, Cremilleux B (2011) Mining dominant patterns in the sky. In: 11th International conference on data mining, IEEE, pp 655\u2013664","DOI":"10.1109\/ICDM.2011.100"},{"key":"674_CR39","unstructured":"Tu S (2014) The Dirichlet-multinomial and Dirichlet-categorical models for Bayesian inference. Tech. rep., Computer Science Division, UC Berkeley"},{"key":"674_CR40","unstructured":"van Leeuwen M, Knobbe AJ (2011) Non-redundant subgroup discovery in large and complex data. In: Gunopulos D, Hofmann T, Malerba D, Vazirgiannis M (eds) Proceedings of the European conference on machine learning and principles and practice of knowledge discovery in databases, ECML PKDD 2011, Springer, vol 6913, pp 459\u2013474"},{"issue":"2","key":"674_CR41","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/s10618-012-0273-y","volume":"25","author":"M van Leeuwen","year":"2012","unstructured":"van Leeuwen M, Knobbe AJ (2012) Diverse subgroup set discovery. Data Min Knowl Discov 25(2):208\u2013242","journal-title":"Data Min Knowl Discov"},{"key":"674_CR42","doi-asserted-by":"crossref","unstructured":"Wu X, Dong Y, Huang C, Xu J, Wang D, Chawla NV (2017) Uapd: Predicting urban anomalies from spatial-temporal data. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 622\u2013638","DOI":"10.1007\/978-3-319-71246-8_38"},{"key":"674_CR43","doi-asserted-by":"crossref","unstructured":"Wang D, Pedreschi D, Song C, Giannotti F, Barabasi AL (2011) Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1100\u20131108","DOI":"10.1145\/2020408.2020581"},{"key":"674_CR44","doi-asserted-by":"crossref","unstructured":"Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 823\u2013831","DOI":"10.1145\/2339530.2339662"},{"key":"674_CR45","doi-asserted-by":"crossref","unstructured":"Yuan Q, Zhang W, Zhang C, Geng X, Cong G, Han J (2017) Pred: Periodic region detection for mobility modeling of social media users. In: Proceedings of the 10th international conference on web search and data mining, ACM, pp 263\u2013272","DOI":"10.1145\/3018661.3018680"},{"issue":"9","key":"674_CR46","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1109\/TKDE.2018.2807840","volume":"30","author":"X Zheng","year":"2018","unstructured":"Zheng X, Han J, Sun A (2018) A survey of location prediction on twitter. IEEE Trans Knowl Data Eng 30(9):1652\u20131671","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"674_CR47","doi-asserted-by":"crossref","unstructured":"Zheng Y, Zhang H, Yu Y (2015) Detecting collective anomalies from multiple spatio-temporal datasets across different domains. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, ACM","DOI":"10.1145\/2820783.2820813"},{"issue":"3","key":"674_CR48","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TBDATA.2016.2586447","volume":"2","author":"Y Zheng","year":"2016","unstructured":"Zheng Y, Wu W, Chen Y, Qu H, Ni LM (2016) Visual analytics in urban computing: an overview. IEEE Transactions on Big Data 2(3):276\u2013296","journal-title":"IEEE Transactions on Big Data"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-020-00674-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-020-00674-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-020-00674-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T21:47:28Z","timestamp":1695678448000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-020-00674-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,29]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["674"],"URL":"https:\/\/doi.org\/10.1007\/s10618-020-00674-z","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,29]]},"assertion":[{"value":"17 September 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}