{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:17:11Z","timestamp":1792783031109,"version":"4.5.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Aalto University","id":[{"id":"https:\/\/ror.org\/020hwjq30","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Count data represented as a matrix of non-negative integer values, such as contingency tables, are prevalent across diverse domains. When clustering such data sets, specific methods are required, as generic algorithms often fail to consider their unique distributional properties, leading to unreliable outputs. An effective strategy is to use well-established statistical models such as the Poisson and negative binomial distributions. We present 3CPO, a clustering algorithm based on statistically solid modeling of count data. In addition to the cluster labels, it identifies a subset of relevant columns, enhancing the interpretability of the results. We propose a simple iterative algorithm that maximizes the posterior probability to find good clustering solutions and discuss its properties. Extensive experiments demonstrate its ability to define high-quality clusters within associated subspaces for various data domains, ranging from gene expressions and texts to economics. Our findings suggest that 3CPO is a robust solution for clustering count data in a statistically sound and interpretable manner. Our code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/collinleiber\/3CPO\" ext-link-type=\"uri\">https:\/\/github.com\/collinleiber\/3CPO<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s10618-026-01230-x","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T04:50:49Z","timestamp":1789966249000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Poisson subspace clustering: focusing on the essentials in count data"],"prefix":"10.1007","volume":"40","author":[{"given":"Collin","family":"Leiber","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Puolam\u00e4ki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heikki","family":"Mannila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"1230_CR1","doi-asserted-by":"crossref","unstructured":"Ailem M, Role F, Nadif M (2015) Co-clustering document-term matrices by direct maximization of graph modularity. In: CIKM, pp. 1807\u20131810. ACM","DOI":"10.1145\/2806416.2806639"},{"key":"1230_CR2","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.patcog.2017.06.005","volume":"72","author":"M Ailem","year":"2017","unstructured":"Ailem M, Role F, Nadif M (2017) Model-based co-clustering for the effective handling of sparse data. Pattern Recognit 72:108\u2013122","journal-title":"Pattern Recognit"},{"issue":"7","key":"1230_CR3","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1109\/TKDE.2017.2681669","volume":"29","author":"M Ailem","year":"2017","unstructured":"Ailem M, Role F, Nadif M (2017) Sparse poisson latent block model for document clustering. IEEE Trans Knowl Data Eng 29(7):1563\u20131576","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1230_CR4","doi-asserted-by":"crossref","unstructured":"Arthur D, Vassilvitskii S (2006) How slow is the k-means method? ACM SCG, pp 144\u2013153","DOI":"10.1145\/1137856.1137880"},{"key":"1230_CR5","unstructured":"Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: SIAM SODA, 1027\u20131035"},{"issue":"2","key":"1230_CR6","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1111\/j.1467-9957.1965.tb00050.x","volume":"33","author":"B Balassa","year":"1965","unstructured":"Balassa B (1965) Trade Liberalisation and Revealed Comparative Advantage. Manch Sch 33(2):99\u2013123","journal-title":"Manch Sch"},{"key":"1230_CR7","doi-asserted-by":"crossref","unstructured":"Bauer LGM, Leiber C, B\u00f6hm C, Plant C (2023) Extension of the dip-test repertoire - efficient and differentiable p-value calculation for clustering. SIAM SDM. pp 109\u2013117","DOI":"10.1137\/1.9781611977653.ch13"},{"key":"1230_CR8","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet Allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"issue":"4","key":"1230_CR9","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1109\/TKDE.2007.190726","volume":"20","author":"N Bouguila","year":"2008","unstructured":"Bouguila N (2008) Clustering of count data using generalized dirichlet multinomial distributions. IEEE Trans Knowl Data Eng 20(4):462\u2013474","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"1230_CR10","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TNN.2010.2091428","volume":"22","author":"N Bouguila","year":"2011","unstructured":"Bouguila N (2011) Count data modeling and classification using finite mixtures of distributions. IEEE Trans Neural Networks 22(2):186\u2013198","journal-title":"IEEE Trans Neural Networks"},{"issue":"4","key":"1230_CR11","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1007\/s10994-023-06474-y","volume":"113","author":"E Battaglia","year":"2024","unstructured":"Battaglia E, Peiretti F, Pensa RG (2024) Fast parameterless prototype-based co-clustering. Mach Learn 113(4):2153\u20132181","journal-title":"Mach Learn"},{"issue":"2","key":"1230_CR12","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1145\/3698875","volume":"57","author":"E Battaglia","year":"2025","unstructured":"Battaglia E, Peiretti F, Pensa RG (2025) Co-clustering: A survey of the main methods, recent trends, and open problems. ACM Comput Surv 57(2):48\u201314833","journal-title":"ACM Comput Surv"},{"key":"1230_CR13","doi-asserted-by":"crossref","unstructured":"Banfield JD, Raftery AE (1993) Model-based gaussian and non-gaussian clustering. Biometrics 803\u2013821","DOI":"10.2307\/2532201"},{"issue":"5\u20136","key":"1230_CR14","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/0191-2607(84)90019-0","volume":"18","author":"A Ceder","year":"1984","unstructured":"Ceder A (1984) Bus frequency determination using passenger count data. Transp Res A 18(5\u20136):439\u2013453","journal-title":"Transp Res A"},{"key":"1230_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2004-5-7-r51","volume":"5","author":"L Cai","year":"2004","unstructured":"Cai L, Huang H, Blackshaw S, Liu JS, Cepko C, Wong WH (2004) Clustering analysis of sage data using a poisson approach. Genome Biol 5:1\u20139","journal-title":"Genome Biol"},{"key":"1230_CR16","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. Association for Computational Linguistics NAACL-HLT (1). pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"1230_CR17","doi-asserted-by":"crossref","unstructured":"Ding CHQ, He X, Zha H, Simon HD (2002) Adaptive dimension reduction for clustering high dimensional data. Proc. IEEE ICDM. pp 147\u2013154","DOI":"10.1109\/ICDM.2002.1183897"},{"issue":"4","key":"1230_CR18","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/0031-3203(76)90045-5","volume":"8","author":"RC Dubes","year":"1976","unstructured":"Dubes RC, Jain AK (1976) Clustering techniques: The user\u2019s dilemma. Pattern Recognit 8(4):247\u2013260","journal-title":"Pattern Recognit"},{"key":"1230_CR19","doi-asserted-by":"crossref","unstructured":"Ding CHQ, Li T (2007) Adaptive dimension reduction using discriminant analysis and K-means clustering. ICML. ACM International Conference Proceeding Series, vol 227. pp 521\u2013528","DOI":"10.1145\/1273496.1273562"},{"issue":"1","key":"1230_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B 39(1):1\u201322","journal-title":"J R Stat Soc Ser B"},{"key":"1230_CR21","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1023\/A:1007612920971","volume":"42","author":"IS Dhillon","year":"2001","unstructured":"Dhillon IS, Modha DS (2001) Concept decompositions for large sparse text data using clustering. Mach Learn 42:143\u2013175","journal-title":"Mach Learn"},{"key":"1230_CR22","doi-asserted-by":"crossref","unstructured":"Elkan C (2006) Clustering documents with an exponential-family approximation of the dirichlet compound multinomial distribution. ICML, vol 148. pp 289\u2013296","DOI":"10.1145\/1143844.1143881"},{"key":"1230_CR23","unstructured":"Farnsworth GL, Nichols JD, Sauer JR, Fancy SG, Pollock KH, Shriner SA, Simons TR, Ralph C, Rich T (2005) Statistical approaches to the analysis of point count data: a little extra information can go a long way. Proc. 3rd Int. Partners Flight Conf, vol 2. pp 736\u2013743"},{"key":"1230_CR24","doi-asserted-by":"crossref","unstructured":"Greene D, Cunningham P (2006) Practical solutions to the problem of diagonal dominance in kernel document clustering. ICML, vol 148. ACM, pp 377\u2013384","DOI":"10.1145\/1143844.1143892"},{"key":"1230_CR25","unstructured":"Gopalan P, Hofman JM, Blei DM (2015) Scalable recommendation with hierarchical poisson factorization. AUAI Press UAI, pp 326\u2013335"},{"key":"1230_CR26","doi-asserted-by":"crossref","unstructured":"Goebl S, He X, Plant C, B\u00f6hm C (2014) Finding the optimal subspace for clustering. Proc. IEEE ICDM. pp 130\u2013139","DOI":"10.1109\/ICDM.2014.34"},{"issue":"3","key":"1230_CR27","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1080\/03610920903140197","volume":"39","author":"G Govaert","year":"2010","unstructured":"Govaert G, Nadif M (2010) Latent block model for contingency table. Communications in Statistics-Theory and Methods 39(3):416\u2013425","journal-title":"Communications in Statistics-Theory and Methods"},{"issue":"3","key":"1230_CR28","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s11634-016-0274-6","volume":"12","author":"G Govaert","year":"2018","unstructured":"Govaert G, Nadif M (2018) Mutual information, phi-squared and model-based co-clustering for contingency tables. Adv Data Anal Classif 12(3):455\u2013488","journal-title":"Adv Data Anal Classif"},{"key":"1230_CR29","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert L, Arabie P (1985) Comparing partitions. J Classif 2:193\u2013218","journal-title":"J Classif"},{"issue":"4","key":"1230_CR30","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1007\/s11634-024-00608-3","volume":"19","author":"S Hoseinipour","year":"2025","unstructured":"Hoseinipour S, Aminghafari M, Mohammadpour A, Nadif M (2025) A sparse exponential family latent block model for co-clustering. Adv Data Anal Classif 19(4):951\u2013987","journal-title":"Adv Data Anal Classif"},{"key":"1230_CR31","volume-title":"Particle counting: How, why, where, & what equipment","author":"EE Hargesheimer","year":"1998","unstructured":"Hargesheimer EE, Lewis CM (1998) Particle counting: How, why, where, & what equipment. Alberta WW Oper, Assoc"},{"key":"1230_CR32","doi-asserted-by":"crossref","unstructured":"Kriegel H-P, Kr\u00f6ger P, Zimek A (2012) Subspace clustering. Wiley Interdiscip Rev Data Min Knowl Discov 2(4):351\u2013364","DOI":"10.1002\/widm.1057"},{"key":"1230_CR33","doi-asserted-by":"crossref","unstructured":"Klein M, Leiber C, B\u00f6hm C (2023) k-submix: Common subspace clustering on mixed-type data. In: ECML\/PKDD (1). Springer Lecture Notes in Computer Science, 14169, 662\u2013677","DOI":"10.1007\/978-3-031-43412-9_39"},{"key":"1230_CR34","unstructured":"Kelly M, Longjohn R, Nottingham K The UCI Machine Learning Repository"},{"issue":"3","key":"1230_CR35","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TSMC.1987.4309069","volume":"17","author":"TO Kvalseth","year":"1987","unstructured":"Kvalseth TO (1987) Entropy and correlation: Some comments. IEEE Trans Syst Man Cybern 17(3):517\u2013519","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"1230_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-8-29","volume":"8","author":"K Kim","year":"2007","unstructured":"Kim K, Zhang S, Jiang K, Cai L, Lee I-B, Feldman LJ, Huang H (2007) Measuring similarities between gene expression profiles through new data transformations. BMC Bioinform 8:1\u201314","journal-title":"BMC Bioinform"},{"issue":"2","key":"1230_CR37","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"SP Lloyd","year":"1982","unstructured":"Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129\u2013136","journal-title":"IEEE Trans Inf Theory"},{"key":"1230_CR38","doi-asserted-by":"crossref","unstructured":"Labiod L, Nadif M (2011) Co-clustering for binary data with maximum modularity. ICONIP 700\u2013708Springer","DOI":"10.1007\/978-3-642-24958-7_81"},{"key":"1230_CR39","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space.arXiv:1301.3781"},{"key":"1230_CR40","doi-asserted-by":"crossref","unstructured":"Manning CD, Raghavan P, Sch\u00fctze H (2008) Introduction to Information Retrieval. Cambridge University Press,","DOI":"10.1017\/CBO9780511809071"},{"key":"1230_CR41","unstructured":"M\u00fcllner D (2011) Modern hierarchical, agglomerative clustering algorithms.arXiv:1109.2378 arXiv preprint"},{"key":"1230_CR42","doi-asserted-by":"crossref","unstructured":"Mautz D, Ye W, Plant C, B\u00f6hm C (2017) Towards an optimal subspace for k-means. Proc. ACM SIGKDD 2017. pp 365\u2013373","DOI":"10.1145\/3097983.3097989"},{"key":"1230_CR43","doi-asserted-by":"crossref","unstructured":"Nadif M, Govaert G (2005) A comparison between block CEM and two-way CEM algorithms to cluster a contingency table. PKDD Springer Lecture Notes in Computer Science 3721:609\u2013616","DOI":"10.1007\/11564126_64"},{"key":"1230_CR44","volume-title":"Do not log-transform count data","author":"R O\u2019Hara","year":"2010","unstructured":"O\u2019Hara R, Kotze J (2010) Do not log-transform count data. Nat, Precedings"},{"key":"1230_CR45","unstructured":"Pelleg D, Moore AW (2000) X-means: Extending k-means with efficient estimation of the number of clusters. ICML. pp 727\u2013734"},{"issue":"11","key":"1230_CR46","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1080\/00036840601019240","volume":"41","author":"J Rouwendal","year":"2009","unstructured":"Rouwendal J, Boter J (2009) Assessing the value of museums with a combined discrete choice\/count data model. Appl Econ 41(11):1417\u20131436","journal-title":"Appl Econ"},{"key":"1230_CR47","unstructured":"Rau A, Celeux G, Martin-Magniette M-L, Maugis-Rabusseau C (2011) Clustering high-throughput sequencing data with poisson mixture models. INRIA (RR-7786 Research Report)"},{"issue":"5","key":"1230_CR48","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen J (1978) Modeling by shortest data description. Automatica 14(5):465\u2013471","journal-title":"Automatica"},{"issue":"2","key":"1230_CR49","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1214\/aos\/1176346150","volume":"11","author":"J Rissanen","year":"1983","unstructured":"Rissanen J (1983) A universal prior for integers and estimation by minimum description length. Ann Stat 11(2):416\u2013431","journal-title":"Ann Stat"},{"key":"1230_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v088.i07","volume":"88","author":"F Role","year":"2019","unstructured":"Role F, Morbieu S, Nadif M (2019) Coclust: a python package for co-clustering. J Stat Softw 88:1\u201329","journal-title":"J Stat Softw"},{"key":"1230_CR51","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput Appl Math 20:53\u201365","journal-title":"Comput Appl Math"},{"issue":"4","key":"1230_CR52","first-page":"333","volume":"3","author":"SE Robertson","year":"2009","unstructured":"Robertson SE, Zaragoza H (2009) The probabilistic relevance framework: BM25 and beyond. Found Trends Inf Retr 3(4):333\u2013389","journal-title":"Found Trends Inf Retr"},{"key":"1230_CR53","doi-asserted-by":"crossref","unstructured":"Satopaa V, Albrecht JR, Irwin D, Raghavan B (2011) Finding a kneedle in a haystack: Detecting knee points in system behavior. ICDCS Workshops. IEEE, pp 166\u2013171","DOI":"10.1109\/ICDCSW.2011.20"},{"key":"1230_CR54","doi-asserted-by":"crossref","unstructured":"Schwarz G (1978) Estimating the dimension of a model. The annals of statistics. pp 461\u2013464","DOI":"10.1214\/aos\/1176344136"},{"issue":"14","key":"1230_CR55","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1136\/vr.161.14.471","volume":"161","author":"L Snow","year":"2007","unstructured":"Snow L, Davies R, Christiansen K, Carrique-Mas J, Wales A, O\u2019connor J, Cook A, Evans S (2007) Survey of the prevalence of salmonella species on commercial laying farms in the united kingdom. Vet Rec 161(14):471\u2013476","journal-title":"Vet Rec"},{"key":"1230_CR56","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/s10618-012-0258-x","volume":"26","author":"K Sim","year":"2013","unstructured":"Sim K, Gopalkrishnan V, Zimek A, Cong G (2013) A survey on enhanced subspace clustering. Data Min Knowl Discov 26:332\u2013397","journal-title":"Data Min Knowl Discov"},{"issue":"1","key":"1230_CR57","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13059-019-1850-9","volume":"21","author":"HTN Tran","year":"2020","unstructured":"Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, Chen J (2020) A benchmark of batch-effect correction methods for single-cell rna sequencing data. Genome Biol 21(1):12","journal-title":"Genome Biol"},{"issue":"4","key":"1230_CR58","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/BF02289263","volume":"18","author":"RL Thorndike","year":"1953","unstructured":"Thorndike RL (1953) Who belongs in the family? Psychometrika 18(4):267\u2013276","journal-title":"Psychometrika"},{"issue":"12","key":"1230_CR59","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1002\/asi.22708","volume":"63","author":"L Waltman","year":"2012","unstructured":"Waltman L, Calero-Medina C, Kosten J, Noyons EC, Tijssen RJ, Eck NJ, Leeuwen TN, Raan AF, Visser MS, Wouters P (2012) The leiden ranking 2011\/2012: Data collection, indicators, and interpretation. J Am Soc Inf Sci Technol 63(12):2419\u20132432","journal-title":"J Am Soc Inf Sci Technol"},{"issue":"4","key":"1230_CR60","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1214\/11-AOAS493","volume":"5","author":"DM Witten","year":"2011","unstructured":"Witten DM (2011) Classification and clustering of sequencing data using a poisson model. Ann Appl Stat 5(4):2493\u20132518","journal-title":"Ann Appl Stat"},{"issue":"3","key":"1230_CR61","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TNN.2005.845141","volume":"16","author":"R Xu","year":"2005","unstructured":"Xu R, Wunsch DC II (2005) Survey of clustering algorithms. IEEE Trans Neural Networks 16(3):645\u2013678","journal-title":"IEEE Trans Neural Networks"},{"issue":"10","key":"1230_CR62","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1109\/TIP.2010.2049235","volume":"19","author":"Y Yang","year":"2010","unstructured":"Yang Y, Xu D, Nie F, Yan S, Zhuang Y (2010) Image clustering using local discriminant models and global integration. IEEE Trans Image Process 19(10):2761\u20132773","journal-title":"IEEE Trans Image Process"},{"key":"1230_CR63","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.ins.2020.03.028","volume":"524","author":"N Zamzami","year":"2020","unstructured":"Zamzami N, Bouguila N (2020) High-dimensional count data clustering based on an exponential approximation to the multinomial beta-liouville distribution. Inf Sci 524:116\u2013135","journal-title":"Inf Sci"},{"issue":"1","key":"1230_CR64","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10044-022-01094-z","volume":"26","author":"N Zamzami","year":"2023","unstructured":"Zamzami N, Bouguila N (2023) A novel minorization-maximization framework for simultaneous feature selection and clustering of high-dimensional count data. Pattern Anal Appl 26(1):91\u2013106","journal-title":"Pattern Anal Appl"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-026-01230-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-026-01230-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-026-01230-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T04:51:27Z","timestamp":1789966287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-026-01230-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":64,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["1230"],"URL":"https:\/\/doi.org\/10.1007\/s10618-026-01230-x","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]},"assertion":[{"value":"30 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no conflict of interest to declare.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"98"}}