{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T22:43:32Z","timestamp":1772923412693,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172293"],"award-info":[{"award-number":["62172293"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s40747-025-01869-x","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T12:53:20Z","timestamp":1744721600000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Attribute grouping-based categorical outlier detection using causal coupling weight"],"prefix":"10.1007","volume":"11","author":[{"given":"Yijing","family":"Song","sequence":"first","affiliation":[]},{"given":"Jianying","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0396-8901","authenticated-orcid":false,"given":"Jifu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"issue":"3","key":"1869_CR1","doi-asserted-by":"publisher","first-page":"2952","DOI":"10.1109\/TPAMI.2022.3188763","volume":"45","author":"S Wang","year":"2023","unstructured":"Wang S, Zeng Y, Yu G et al (2023) E$$^{3}$$3outlier: a self-supervised framework for unsupervised deep outlier detection. IEEE Trans Pattern Anal Mach Intell 45(3):2952\u20132969. https:\/\/doi.org\/10.1109\/TPAMI.2022.3188763","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1869_CR2","doi-asserted-by":"publisher","unstructured":"Li Y, Chen Z, Zha D et\u00a0al (2021) AutoOD: neural architecture search for outlier detection. In: 37th IEEE international conference on data engineering, ICDE 2021, Chania, Greece, April 19\u201322, 2021. IEEE, pp 2117\u20132122. https:\/\/doi.org\/10.1109\/ICDE51399.2021.00210","DOI":"10.1109\/ICDE51399.2021.00210"},{"issue":"7","key":"1869_CR3","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.14778\/3523210.3523225","volume":"15","author":"X Han","year":"2022","unstructured":"Han X, Cheng R, Ma C et al (2022) DeepTEA: effective and efficient online time-dependent trajectory outlier detection. Proc VLDB Endow 15(7):1493\u20131505. https:\/\/doi.org\/10.14778\/3523210.3523225","journal-title":"Proc VLDB Endow"},{"issue":"1","key":"1869_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TSMC.2022.3167838","volume":"53","author":"M Wang","year":"2023","unstructured":"Wang M, Zhou D, Chen M (2023) Anomaly monitoring of nonstationary processes with continuous and two-valued variables. IEEE Trans Syst Man Cybern Syst 53(1):49\u201358. https:\/\/doi.org\/10.1109\/TSMC.2022.3167838","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1","key":"1869_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2021.116212","volume":"191","author":"Y Yang","year":"2022","unstructured":"Yang Y, Fan C, Chen L et al (2022) IPMOD: an efficient outlier detection model for high-dimensional medical data streams. Expert Syst Appl 191(1):116212. https:\/\/doi.org\/10.1016\/J.ESWA.2021.116212","journal-title":"Expert Syst Appl"},{"issue":"16","key":"1869_CR6","doi-asserted-by":"publisher","first-page":"4011","DOI":"10.1093\/BIOINFORMATICS\/BTAC431","volume":"38","author":"P Dey","year":"2022","unstructured":"Dey P, Zhang Z, Dunson DB (2022) Outlier detection for multi-network data. Bioinformatics 38(16):4011\u20134018. https:\/\/doi.org\/10.1093\/BIOINFORMATICS\/BTAC431","journal-title":"Bioinformatics"},{"issue":"4","key":"1869_CR7","doi-asserted-by":"publisher","first-page":"6440","DOI":"10.1109\/TIP.2022.3211476","volume":"31","author":"N Huyan","year":"2022","unstructured":"Huyan N, Quan D, Zhang X et al (2022) Unsupervised outlier detection using memory and contrastive learning. IEEE Trans Image Process 31(4):6440\u20136454. https:\/\/doi.org\/10.1109\/TIP.2022.3211476","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"1869_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2023.109334","volume":"138","author":"A Mensi","year":"2023","unstructured":"Mensi A, Tax DMJ, Bicego M (2023) Detecting outliers from pairwise proximities: proximity isolation forests. Pattern Recognit 138(6):109334. https:\/\/doi.org\/10.1016\/J.PATCOG.2023.109334","journal-title":"Pattern Recognit"},{"issue":"2","key":"1869_CR9","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1109\/TKDE.2021.3103571","volume":"35","author":"M Toller","year":"2023","unstructured":"Toller M, Geiger BC, Kern R (2023) Cluster purging: efficient outlier detection based on rate-distortion theory. IEEE Trans Knowl Data Eng 35(2):1270\u20131282. https:\/\/doi.org\/10.1109\/TKDE.2021.3103571","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1869_CR10","doi-asserted-by":"publisher","unstructured":"Pang G, Xu H, Cao L et\u00a0al (2017) Selective value coupling learning for detecting outliers in high-dimensional categorical data. In: Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM 2017, Singapore, November 06\u201310, 2017. ACM, pp 807\u2013816. https:\/\/doi.org\/10.1145\/3132847.3132994","DOI":"10.1145\/3132847.3132994"},{"issue":"11","key":"1869_CR11","doi-asserted-by":"publisher","first-page":"4295","DOI":"10.1109\/TSMC.2018.2847625","volume":"50","author":"J Li","year":"2020","unstructured":"Li J, Zhang J, Pang N et al (2020) Weighted outlier detection of high-dimensional categorical data using feature grouping. IEEE Trans Syst Man Cybern Syst 50(11):4295\u20134308. https:\/\/doi.org\/10.1109\/TSMC.2018.2847625","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1","key":"1869_CR12","doi-asserted-by":"publisher","first-page":"103","DOI":"10.2298\/CSIS0501103H","volume":"2","author":"Z He","year":"2005","unstructured":"He Z, Xu X, Huang JZ et al (2005) FP-outlier: frequent pattern based outlier detection. Comput Sci Inf Syst 2(1):103\u2013118. https:\/\/doi.org\/10.2298\/CSIS0501103H","journal-title":"Comput Sci Inf Syst"},{"issue":"3","key":"1869_CR13","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1109\/TKDE.2011.261","volume":"25","author":"S Wu","year":"2013","unstructured":"Wu S, Wang S (2013) Information-theoretic outlier detection for large-scale categorical data. IEEE Trans Knowl Data Eng 25(3):589\u2013602. https:\/\/doi.org\/10.1109\/TKDE.2011.261","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"1869_CR14","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1109\/TNNLS.2016.2526063","volume":"28","author":"D Ienco","year":"2017","unstructured":"Ienco D, Pensa RG, Meo R (2017) A semisupervised approach to the detection and characterization of outliers in categorical data. IEEE Trans Neural Netw Learn Syst 28(5):1017\u20131029. https:\/\/doi.org\/10.1109\/TNNLS.2016.2526063","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"#cr-split#-1869_CR15.1","unstructured":"Pang G, Cao L, Chen L (2016) Outlier detection in complex categorical data by modeling the feature value couplings. In: Kambhampati S"},{"key":"#cr-split#-1869_CR15.2","unstructured":"(ed) Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016. IJCAI\/AAAI Press, pp 1902-1908"},{"key":"1869_CR16","doi-asserted-by":"publisher","unstructured":"Xu H, Wang Y, Wu Z et\u00a0al (2019) Embedding-based complex feature value coupling learning for detecting outliers in non-IID categorical data. In: The thirty-third AAAI conference on artificial intelligence, AAAI 2019, the thirty-first innovative applications of artificial intelligence conference, IAAI 2019, the ninth AAAI symposium on educational advances in artificial intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27\u2013February 1, 2019. AAAI Press, pp 5541\u20135548. https:\/\/doi.org\/10.1609\/AAAI.V33I01.33015541","DOI":"10.1609\/AAAI.V33I01.33015541"},{"issue":"3","key":"1869_CR17","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/TSMC.2017.2718592","volume":"50","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yu X, Xun Y et al (2020) Scalable mining of contextual outliers using relevant subspace. IEEE Trans Syst Man Cybern Syst 50(3):988\u20131002. https:\/\/doi.org\/10.1109\/TSMC.2017.2718592","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1869_CR18","doi-asserted-by":"publisher","unstructured":"Xiang H, Zhang X, Hu H et\u00a0al (2023) OptIForest: optimal isolation forest for anomaly detection. In: Proceedings of the thirty-second international joint conference on artificial intelligence, IJCAI 2023, 19th\u201325th August 2023, Macao, SAR, China. ijcai.org, pp 2379\u20132387. https:\/\/doi.org\/10.24963\/IJCAI.2023\/264","DOI":"10.24963\/IJCAI.2023\/264"},{"key":"1869_CR19","unstructured":"Huang B, Zhang K, Xie P et\u00a0al (2019) Specific and shared causal relation modeling and mechanism-based clustering. In: Wallach HM, Larochelle H, Beygelzimer A et\u00a0al (eds) Advances in neural information processing systems 32: annual conference on neural information processing systems 2019, NeurIPS 2019, December 8\u201314, 2019, Vancouver, BC, Canada, pp 13510\u201313521"},{"issue":"5721","key":"1869_CR20","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1126\/science.1105809","volume":"308","author":"K Sachs","year":"2005","unstructured":"Sachs K, Perez O, Pe\u2019er D et al (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science (New York, NY) 308(5721):523\u20139. https:\/\/doi.org\/10.1126\/science.1105809","journal-title":"Science (New York, NY)"},{"issue":"11","key":"1869_CR21","doi-asserted-by":"publisher","first-page":"6913","DOI":"10.1007\/S00521-019-04161-5","volume":"32","author":"W Chen","year":"2020","unstructured":"Chen W, Cai R, Hao Z et al (2020) Mining hidden non-redundant causal relationships in online social networks. Neural Comput Appl 32(11):6913\u20136923. https:\/\/doi.org\/10.1007\/S00521-019-04161-5","journal-title":"Neural Comput Appl"},{"key":"1869_CR22","doi-asserted-by":"publisher","DOI":"10.1145\/3595380","author":"J Yuan","year":"2023","unstructured":"Yuan J, Ma X, Xiong R et al (2023) Instrumental variable-driven domain generalization with unobserved confounders. ACM Trans Knowl Discov Data. https:\/\/doi.org\/10.1145\/3595380","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1869_CR23","unstructured":"Chen W, Wu Y, Cai R et\u00a0al (2021) CCSL: a causal structure learning method from multiple unknown environments. Comput Res Repos. arXiv:2111.09666"},{"key":"1869_CR24","unstructured":"Compton S, Kocaoglu M, Greenewald KH et\u00a0al (2020) Entropic causal inference: identifiability and finite sample results. In: Larochelle H, Ranzato M, Hadsell R et\u00a0al (eds) Advances in neural information processing systems 33: annual conference on neural information processing systems 2020, NeurIPS 2020, December 6\u201312, 2020, virtual"},{"key":"1869_CR25","unstructured":"Cai R, Qiao J, Zhang K et\u00a0al (2018) Causal discovery from discrete data using hidden compact representation. In: Bengio S, Wallach HM, Larochelle H et\u00a0al (eds) Advances in neural information processing systems 31: annual conference on neural information processing systems 2018, NeurIPS 2018, December 3\u20138, 2018, Montr\u00e9al, Canada, pp 2671\u20132679"},{"key":"1869_CR26","doi-asserted-by":"publisher","unstructured":"Mohan A, Leow WX, Hobor A (2021) Functional correctness of C implementations of Dijkstra\u2019s, Kruskal\u2019s, and Prim\u2019s algorithms. In: Silva A, Leino KRM (eds) Computer aided verification\u201433rd international conference, CAV 2021, Virtual Event, July 20\u201323, 2021, proceedings, Part II, lecture notes in computer science, vol 12760. Springer, pp 801\u2013826. https:\/\/doi.org\/10.1007\/978-3-030-81688-9_37","DOI":"10.1007\/978-3-030-81688-9_37"},{"key":"1869_CR27","doi-asserted-by":"publisher","unstructured":"Tang J, Qu M, Wang M et\u00a0al (2015) LINE: large-scale information network embedding. In: Gangemi A, Leonardi S, Panconesi A (eds) Proceedings of the 24th international conference on World Wide Web, WWW 2015, Florence, Italy, May 18\u201322, 2015. ACM, pp 1067\u20131077. https:\/\/doi.org\/10.1145\/2736277.2741093","DOI":"10.1145\/2736277.2741093"},{"key":"1869_CR28","doi-asserted-by":"publisher","unstructured":"Feng Y, Zhao S, Zhang Y et\u00a0al (2022) Noise-tolerant learning with silhouette coefficient for unsupervised person re-identification. In: IEEE international conference on multimedia and expo, ICME 2022, Taipei, Taiwan, July 18\u201322, 2022. IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME52920.2022.9859824","DOI":"10.1109\/ICME52920.2022.9859824"},{"key":"1869_CR29","doi-asserted-by":"publisher","first-page":"613","DOI":"10.5555\/1314498.1314520","volume":"8","author":"M Kalisch","year":"2007","unstructured":"Kalisch M, B\u00fchlmann P (2007) Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J Mach Learn Res 8:613\u2013636. https:\/\/doi.org\/10.5555\/1314498.1314520","journal-title":"J Mach Learn Res"},{"key":"1869_CR30","unstructured":"Cai R, Qiao J, Zhang K et\u00a0al (2018) Causal discovery from discrete data using hidden compact representation. In: Advances in neural information processing systems 31: annual conference on neural information processing systems 2018, NeurIPS 2018, December 3\u20138, 2018, Montr\u00e9al, Canada, pp 2671\u20132679"},{"issue":"6","key":"1869_CR31","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.78.065102","volume":"78","author":"J G\u00f3mez-Garde\u00f1es","year":"2009","unstructured":"G\u00f3mez-Garde\u00f1es J, Latora V (2009) Entropy rate of diffusion processes on complex networks. Phys Rev E Stat Nonlinear Soft Matter Phys 78(6):065102. https:\/\/doi.org\/10.1103\/PhysRevE.78.065102","journal-title":"Phys Rev E Stat Nonlinear Soft Matter Phys"},{"key":"1869_CR32","doi-asserted-by":"publisher","unstructured":"Chen J, Feng M, Zhao D et al (2023) Composite effective degree Markov chain for epidemic dynamics on higher-order networks. IEEE Trans Syst Man Cybern Syst 53(12):7415\u20137426. https:\/\/doi.org\/10.1109\/TSMC.2023.3298019","DOI":"10.1109\/TSMC.2023.3298019"},{"key":"1869_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJAR.2023.109086","volume":"164","author":"Z Zhao","year":"2024","unstructured":"Zhao Z, Wang R, Huang D et al (2024) Outlier detection for partially labeled categorical data based on conditional information entropy. Int J Approx Reason 164:109086. https:\/\/doi.org\/10.1016\/J.IJAR.2023.109086","journal-title":"Int J Approx Reason"},{"issue":"4","key":"1869_CR34","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1007\/S10618-021-00750-Y","volume":"35","author":"G Pang","year":"2021","unstructured":"Pang G, Cao L, Chen L (2021) Homophily outlier detection in non-IID categorical data. Data Min Knowl Discov 35(4):1163\u20131224. https:\/\/doi.org\/10.1007\/S10618-021-00750-Y","journal-title":"Data Min Knowl Discov"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01869-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01869-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01869-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T11:22:20Z","timestamp":1747480940000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01869-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":35,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1869"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01869-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,15]]},"assertion":[{"value":"17 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest or competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"240"}}