{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:28:58Z","timestamp":1760171338932,"version":"3.37.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030307592"},{"type":"electronic","value":"9783030307608"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30760-8_2","type":"book-chapter","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T13:02:54Z","timestamp":1568034174000},"page":"18-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Unsupervised Method for Concept Association Analysis in Text Collections"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1424-6995","authenticated-orcid":false,"given":"Pavlo","family":"Kovalchuk","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3671-9637","authenticated-orcid":false,"given":"Diogo","family":"Proen\u00e7a","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5463-8438","authenticated-orcid":false,"given":"Jos\u00e9","family":"Borbinha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3993-0171","authenticated-orcid":false,"given":"Rui","family":"Henriques","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,30]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Abualigah, L.M., Khader, A.T., Al-Betar, M.A., Awadallah, M.A.: A krill herd algorithm for efficient text documents clustering. In: 2016 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 67\u201372. IEEE (2016)","DOI":"10.1109\/ISCAIE.2016.7575039"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Amig\u00f3, E., Gonzalo, J., Verdejo, F.: A general evaluation measure for document organization tasks. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 643\u2013652. ACM (2013)","DOI":"10.1145\/2484028.2484081"},{"key":"2_CR3","unstructured":"Boudin, F.: Pke: an open source python-based keyphrase extraction toolkit. In: COLING, Osaka, Japan, pp. 69\u201373 (2016)"},{"key":"2_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/0470011297","volume-title":"Concept Data Analysis: Theory and Applications","author":"C Carpineto","year":"2004","unstructured":"Carpineto, C., Romano, G.: Concept Data Analysis: Theory and Applications. Wiley, Hoboken (2004)"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.is.2017.01.008","volume":"66","author":"A Castellanos","year":"2017","unstructured":"Castellanos, A., Cigarr\u00e1n, J., Garc\u00eda-Serrano, A.: Formal concept analysis for topic detection: a clustering quality experimental analysis. Inf. Syst. 66, 24\u201342 (2017)","journal-title":"Inf. Syst."},{"issue":"2","key":"2_CR6","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1002\/asi.22767","volume":"64","author":"YL Chen","year":"2013","unstructured":"Chen, Y.L., Liu, Y.H., Ho, W.L.: A text mining approach to assist the general public in the retrieval of legal documents. IJ Am. Soc. Inf. Sci. Technol. 64(2), 280\u2013290 (2013)","journal-title":"IJ Am. Soc. Inf. Sci. Technol."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Cutting, D.R., Karger, D.R., Pedersen, J.O., Tukey, J.W.: Scatter\/Gather: a cluster-based approach to browsing large document collections. In: ACM SIGIR, pp. 318\u2013329. ACM (1992)","DOI":"10.1145\/133160.133214"},{"issue":"2","key":"2_CR8","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s11704-009-0062-y","volume":"4","author":"A Daud","year":"2010","unstructured":"Daud, A., Li, J., Zhou, L., Muhammad, F.: Knowledge discovery through directed probabilistic topic models: a survey. Front. Comput. Sci. China 4(2), 280\u2013301 (2010)","journal-title":"Front. Comput. Sci. China"},{"issue":"2","key":"2_CR9","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"PAMI-1","author":"David L. Davies","year":"1979","unstructured":"Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224\u2013227 (1979)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"2_CR10","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01969727408546059","volume":"4","author":"JC Dunn","year":"1974","unstructured":"Dunn, J.C.: Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95\u2013104 (1974)","journal-title":"J. Cybern."},{"issue":"3","key":"2_CR11","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1093\/comjnl\/32.3.220","volume":"32","author":"A El-Hamdouchi","year":"1989","unstructured":"El-Hamdouchi, A., Willett, P.: Comparison of hierarchic agglomerative clustering methods for document retrieval. Comput. J. 32(3), 220\u2013227 (1989)","journal-title":"Comput. J."},{"issue":"12","key":"2_CR12","doi-asserted-by":"publisher","first-page":"4831","DOI":"10.1016\/j.cnsns.2012.05.010","volume":"17","author":"AH Gandomi","year":"2012","unstructured":"Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831\u20134845 (2012)","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"2_CR13","unstructured":"Gon\u00e7alves, T., Quaresma, P.: Evaluating preprocessing techniques in a text classification problem. SBC-Sociedade Brasileira de Computa\u00e7\u00e3o, S\u00e3o Leopoldo, RS, Brasil (2005)"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/s10618-017-0521-2","volume":"32","author":"R Henriques","year":"2017","unstructured":"Henriques, R., Madeira, S.C.: BSig: evaluating the statistical significance of biclustering solutions. Data Min. Knowl. Discov. 32, 124\u2013161 (2017)","journal-title":"Data Min. Knowl. Discov."},{"key":"2_CR15","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/978-3-319-25485-2_3","volume-title":"Information Retrieval","author":"DI Ignatov","year":"2015","unstructured":"Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. In: Braslavski, P., Karpov, N., Worring, M., Volkovich, Y., Ignatov, D.I. (eds.) RuSSIR 2014. CCIS, vol. 505, pp. 42\u2013141. Springer, Cham (2015). \n                      https:\/\/doi.org\/10.1007\/978-3-319-25485-2_3"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Jaganathan, P., Jaiganesh, S.: An improved k-means algorithm combined with particle swarm optimization approach for efficient web document clustering. In: ICGCE, pp. 772\u2013776. IEEE (2013)","DOI":"10.1109\/ICGCE.2013.6823538"},{"issue":"1","key":"2_CR17","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1016\/j.eswa.2011.08.040","volume":"39","author":"S Jiang","year":"2012","unstructured":"Jiang, S., Pang, G., Wu, M., Kuang, L.: An improved k-nearest-neighbor algorithm for text categorization. Expert Syst. Appl. 39(1), 1503\u20131509 (2012)","journal-title":"Expert Syst. Appl."},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Jin, W., Srihari, R.K., Ho, H.H., Wu, X.: Improving knowledge discovery in document collections through combining text retrieval and link analysis techniques. In: ICDM, pp. 193\u2013202 (2007)","DOI":"10.1109\/ICDM.2007.62"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Kadhim, A.I., Cheah, Y.N., Ahamed, N.H.: Text document preprocessing and dimension reduction techniques for text document clustering. In: 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, pp. 69\u201373. IEEE (2014)","DOI":"10.1109\/ICAIET.2014.21"},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1080\/07468342.1996.11973744","volume":"27","author":"D Kalman","year":"1996","unstructured":"Kalman, D.: A singularly valuable decomposition: the SVD of a matrix. Coll. Math. J. 27(1), 2\u201323 (1996)","journal-title":"Coll. Math. J."},{"key":"2_CR21","unstructured":"Karypis, M.S.G., Kumar, V., Steinbach, M.: A comparison of document clustering techniques. In: IW on Text Mining at SIGKDD (2000)"},{"issue":"12","key":"2_CR22","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1080\/03610926.2011.560741","volume":"41","author":"M Kozak","year":"2012","unstructured":"Kozak, M.: \u201cA dendrite method for cluster analysis\u201d by Cali\u0144ski and Harabasz: a classical work that is far too often incorrectly cited. Commun. Stat.-Theory Methods 41(12), 2279\u20132280 (2012)","journal-title":"Commun. Stat.-Theory Methods"},{"key":"2_CR23","unstructured":"Kuzuetsov, S.: Stability as an estimate of the degree of substantiation of hypotheses derived on the basis of operational, similarity (1990)"},{"issue":"2\u20133","key":"2_CR24","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1080\/01638539809545028","volume":"25","author":"TK Landauer","year":"1998","unstructured":"Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2\u20133), 259\u2013284 (1998)","journal-title":"Discourse Process."},{"issue":"1","key":"2_CR25","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1016\/j.eswa.2011.07.070","volume":"39","author":"CH Li","year":"2012","unstructured":"Li, C.H., Yang, J.C., Park, S.C.: Text categorization algorithms using semantic approaches, corpus-based thesaurus and wordnet. Expert Syst. Appl. 39(1), 765\u2013772 (2012)","journal-title":"Expert Syst. Appl."},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Jin, W.: Cross-document knowledge discovery using semantic concept topic model. In: ICMLA, pp. 108\u2013114. IEEE (2016)","DOI":"10.1109\/ICMLA.2016.0026"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Mishra, R.K., Saini, K., Bagri, S.: Text document clustering on the basis of inter passage approach by using k-means. In: IC on Computing, Communication and Automation, pp. 110\u2013113. IEEE (2015)","DOI":"10.1109\/CCAA.2015.7148354"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Myat, N.N., Hla, K.H.S.: Organizing web documents resulting from an information retrieval system using formal concept analysis. In: Asia-Pacific Symposium on Information and Telecommunication Technologies, pp. 198\u2013203. IEEE (2005)","DOI":"10.1109\/APSITT.2005.203656"},{"key":"2_CR29","unstructured":"Quan, T.T., Hui, S.C., Cao, T.H.: A fuzzy FCA-based approach to conceptual clustering for automatic generation of concept hierarchy on uncertainty data. In: CLA, pp. 1\u201312 (2004)"},{"issue":"1","key":"2_CR30","first-page":"34","volume":"2","author":"K Raghuveer","year":"2012","unstructured":"Raghuveer, K.: Legal documents clustering using latent dirichlet allocation. IAES Int. J. Artif. Intell. 2(1), 34\u201337 (2012)","journal-title":"IAES Int. J. Artif. Intell."},{"key":"2_CR31","first-page":"1","volume-title":"Data Mining","author":"A Rajaraman","year":"2011","unstructured":"Rajaraman, A., Ullman, J.D.: Data Mining, pp. 1\u201317. Cambridge University Press, Cambridge (2011)"},{"key":"2_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"2_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/BFb0040810","volume-title":"Evolutionary Programming VII","author":"Y Shi","year":"1998","unstructured":"Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591\u2013600. Springer, Heidelberg (1998). \n                      https:\/\/doi.org\/10.1007\/BFb0040810"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Singh, V.K., Tiwari, N., Garg, S.: Document clustering using k-means, heuristic k-means and fuzzy c-means. In: IC on Computational Intelligence and Communication Networks, pp. 297\u2013301. IEEE (2011)","DOI":"10.1109\/CICN.2011.62"},{"issue":"11","key":"2_CR35","first-page":"49","volume":"47","author":"V Srividhya","year":"2010","unstructured":"Srividhya, V., Anitha, R.: Evaluating preprocessing techniques in text categorization. Int. J. Comput. Sci. Appl. 47(11), 49\u201351 (2010)","journal-title":"Int. J. Comput. Sci. Appl."},{"key":"2_CR36","unstructured":"Stevens, K., Kegelmeyer, P., Andrzejewski, D., Buttler, D.: Exploring topic coherence over many models and many topics. In: Joint Conference on Empirical Methods in NLP and Computational Natural Language Learning, pp. 952\u2013961. Association for Computational Linguistics (2012)"},{"key":"2_CR37","volume-title":"Introduction to Data Mining","author":"PN Tan","year":"2018","unstructured":"Tan, P.N.: Introduction to Data Mining. Pearson Education, Delhi (2018)"},{"key":"2_CR38","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/978-3-540-24651-0_31","volume-title":"Concept Lattices","author":"D Merwe van der","year":"2004","unstructured":"van der Merwe, D., Obiedkov, S., Kourie, D.: AddIntent: a new incremental algorithm for constructing concept lattices. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 372\u2013385. Springer, Heidelberg (2004). \n                      https:\/\/doi.org\/10.1007\/978-3-540-24651-0_31"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Venkatesh, R.K.: Legal documents clustering and summarization using hierarchical latent Dirichlet allocation. IAES Int. J. Artif. Intell. 2(1) (2013)","DOI":"10.11591\/ij-ai.v2i1.1186"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Wang, X., McCallum, A., Wei, X.: Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: ICDM, pp. 697\u2013702. IEEE (2007)","DOI":"10.1109\/ICDM.2007.86"},{"key":"2_CR41","series-title":"NATO Advanced Study Institutes Series (Series C \u2014 Mathematical and Physical Sciences)","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-94-009-7798-3_15","volume-title":"Ordered Sets","author":"R Wille","year":"1982","unstructured":"Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets. ASIC, vol. 83, pp. 445\u2013470. Springer, Dordrecht (1982). \n                      https:\/\/doi.org\/10.1007\/978-94-009-7798-3_15"}],"container-title":["Lecture Notes in Computer Science","Digital Libraries for Open Knowledge"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30760-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T13:03:28Z","timestamp":1568034208000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30760-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030307592","9783030307608"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30760-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TPDL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Theory and Practice of Digital Libraries","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oslo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tpdl2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tpdl.eu\/tpdl2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}