{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:38:21Z","timestamp":1769758701798,"version":"3.49.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031301100","type":"print"},{"value":"9783031301117","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30111-7_55","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T05:02:51Z","timestamp":1681275771000},"page":"659-670","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Hybrid Feature Selection Approach for\u00a0Data Clustering Based on\u00a0Ant Colony Optimization"],"prefix":"10.1007","author":[{"given":"Rajesh","family":"Dwivedi","sequence":"first","affiliation":[]},{"given":"Aruna","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Neha","family":"Bharill","sequence":"additional","affiliation":[]},{"given":"Milind","family":"Ratnaparkhe","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"55_CR1","unstructured":"Blake, C.: UCI repository of machine learning databases (1998). http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html"},{"key":"55_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1007\/3-540-45571-X_13","volume-title":"Knowledge Discovery and Data Mining. Current Issues and New Applications","author":"M Dash","year":"2000","unstructured":"Dash, M., Liu, H.: Feature selection for clustering. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS (LNAI), vol. 1805, pp. 110\u2013121. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45571-X_13"},{"issue":"1","key":"55_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/4235.585892","volume":"1","author":"M Dorigo","year":"1997","unstructured":"Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53\u201366 (1997)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"55_CR4","first-page":"35","volume":"7","author":"R Dwivedi","year":"2019","unstructured":"Dwivedi, R., Kumar, R., Jangam, E., Kumar, V.: An ant colony optimization based feature selection for data classification. Int. J. Recent Technol. Eng. 7, 35\u201340 (2019)","journal-title":"Int. J. Recent Technol. Eng."},{"issue":"7073","key":"55_CR5","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/439153a","volume":"439","author":"NR Franks","year":"2006","unstructured":"Franks, N.R., Richardson, T.: Teaching in tandem-running ants. Nature 439(7073), 153 (2006)","journal-title":"Nature"},{"key":"55_CR6","unstructured":"He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. Adv. Neural Inf. Process. Syst. 18, 507\u2013514 (2005)"},{"key":"55_CR7","doi-asserted-by":"crossref","unstructured":"Hruschka, E.R., Covoes, T.F., Ebecken, N.F.: Feature selection for clustering problems: a hybrid algorithm that iterates between k-means and a Bayesian filter. In: Fifth International Conference on Hybrid Intelligent Systems (HIS 2005), pp. 6-pp. IEEE (2005)","DOI":"10.1109\/ICHIS.2005.42"},{"key":"55_CR8","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/978-981-13-9942-8_7","volume-title":"Advances in Computing and Data Sciences","author":"R Kumar","year":"2019","unstructured":"Kumar, R., Dwivedi, R., Jangam, E.: Hybrid fuzzy C-means using bat optimization and maxi-min distance classifier. In: Singh, M., Gupta, P.K., Tyagi, V., Flusser, J., \u00d6ren, T., Kashyap, R. (eds.) ICACDS 2019. CCIS, vol. 1046, pp. 68\u201379. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-9942-8_7"},{"key":"55_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Lu, B.L., Wu, Z.F.: A hybrid method of unsupervised feature selection based on ranking. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 687\u2013690. IEEE (2006)","DOI":"10.1109\/ICPR.2006.84"},{"key":"55_CR10","doi-asserted-by":"publisher","unstructured":"Nahato, K.B., Harichandran, K.N., Arputharaj, K.: Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Computat. Math. Methods Med. 2015 (2015). https:\/\/doi.org\/10.1155\/2015\/460189","DOI":"10.1155\/2015\/460189"},{"key":"55_CR11","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-981-33-4305-4_22","volume-title":"Inventive Computation and Information Technologies","author":"N Nayar","year":"2021","unstructured":"Nayar, N., Gautam, S., Singh, P., Mehta, G.: Ant colony optimization: a review of literature and application in feature selection. In: Smys, S., Balas, V.E., Kamel, K.A., Lafata, P. (eds.) Inventive Computation and Information Technologies. LNNS, vol. 173, pp. 285\u2013297. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-33-4305-4_22"},{"issue":"3","key":"55_CR12","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1093\/sysbio\/45.3.380","volume":"45","author":"R Real","year":"1996","unstructured":"Real, R., Vargas, J.M.: The probabilistic basis of Jaccard\u2019s index of similarity. Syst. Biol. 45(3), 380\u2013385 (1996)","journal-title":"Syst. Biol."},{"key":"55_CR13","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":"55_CR14","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.neucom.2016.07.026","volume":"214","author":"S Solorio-Fern\u00e1ndez","year":"2016","unstructured":"Solorio-Fern\u00e1ndez, S., Carrasco-Ochoa, J.A., Mart\u00ednez-Trinidad, J.F.: A new hybrid filter-wrapper feature selection method for clustering based on ranking. Neurocomputing 214, 866\u2013880 (2016)","journal-title":"Neurocomputing"},{"issue":"2","key":"55_CR15","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1007\/s10462-019-09682-y","volume":"53","author":"S Solorio-Fern\u00e1ndez","year":"2020","unstructured":"Solorio-Fern\u00e1ndez, S., Carrasco-Ochoa, J.A., Mart\u00ednez-Trinidad, J.F.: A review of unsupervised feature selection methods. Artif. Intell. Rev. 53(2), 907\u2013948 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"55_CR16","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.cmpb.2017.04.009","volume":"145","author":"JD Sweetlin","year":"2017","unstructured":"Sweetlin, J.D., Nehemiah, H.K., Kannan, A.: Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Comput. Methods Programs Biomed. 145, 115\u2013125 (2017)","journal-title":"Comput. Methods Programs Biomed."},{"key":"55_CR17","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.engappai.2014.03.007","volume":"32","author":"S Tabakhi","year":"2014","unstructured":"Tabakhi, S., Moradi, P., Akhlaghian, F.: An unsupervised feature selection algorithm based on ant colony optimization. Eng. Appl. Artif. Intell. 32, 112\u2013123 (2014)","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30111-7_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T03:26:50Z","timestamp":1729222010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30111-7_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301100","9783031301117"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30111-7_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"0","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":"44% - 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":"2.65","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}