{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T12:01:53Z","timestamp":1747224113403,"version":"3.40.5"},"reference-count":24,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,1,1]]},"abstract":"<p>Biclustering methods are the potential data mining technique that has been suggested to identify local patterns in the data. Biclustering algorithms are used for mining the web usage data which can determine a group of users which are correlated under a subset of pages of a web site. Recently, many blistering methods based on meta-heuristics have been proposed. Most use the Mean Squared Residue as merit function but interesting and relevant patterns such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of pattern since commonly the web users can present a similar behavior although their interest levels vary in different ranges or magnitudes. In this paper a new correlation based fitness function is designed to extract shifting and scaling browsing patterns. The proposed work uses a discrete version of Artificial Bee Colony optimization algorithm for biclustering of web usage data to produce optimal biclusters (i.e., highly correlated biclusters). It\u2019s demonstrated on real dataset and its results show that proposed approach can find significant biclusters of high quality and has better convergence performance than Binary Particle Swarm Optimization (BPSO).<\/p>","DOI":"10.4018\/jsir.2012010102","type":"journal-article","created":{"date-parts":[[2012,5,31]],"date-time":"2012-05-31T18:41:56Z","timestamp":1338489716000},"page":"30-42","source":"Crossref","is-referenced-by-count":4,"title":["A Discrete Artificial Bees Colony Inspired Biclustering Algorithm"],"prefix":"10.4018","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3970-262X","authenticated-orcid":true,"given":"R.","family":"Rathipriya","sequence":"first","affiliation":[{"name":"Periyar University, India"}]},{"given":"K.","family":"Thangavel","sequence":"additional","affiliation":[{"name":"Periyar University, India"}]}],"member":"2432","reference":[{"key":"jsir.2012010102-0","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti641"},{"key":"jsir.2012010102-1","doi-asserted-by":"publisher","DOI":"10.1145\/1183081.1183082"},{"key":"jsir.2012010102-2","doi-asserted-by":"crossref","unstructured":"Bleuler, S., Prelic, A., & Zitzler, E. (2004). An EA framework for biclustering of gene expression data. In Proceedings of the Congress on Evolutionary Computation (Vol. 1, pp. 166-173).","DOI":"10.1109\/CEC.2004.1330853"},{"key":"jsir.2012010102-3","doi-asserted-by":"crossref","unstructured":"Bryan, K. (2005). Biclustering of expression data using simulated annealing. In Proceedings of the 18th IEEE International Symposium on Computer-Based Medical Systems (pp. 383-388).","DOI":"10.1109\/CBMS.2005.37"},{"key":"jsir.2012010102-4","unstructured":"Busygin, S., Jacobsen, G., & Kramer, E. (2002). Double conjugated clustering applied to leukemia microarray data. In Proceedings of the SIAM Data Mining Workshop on Clustering High Dimensional Data and its Applications."},{"key":"jsir.2012010102-5","doi-asserted-by":"crossref","unstructured":"Chakraborty, A., & Maka, H. (2005). Biclustering of gene expression data using genetic algorithm. 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In Proceedings of the International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (pp. 1-9)."},{"key":"jsir.2012010102-9","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-S1-S27"},{"key":"jsir.2012010102-10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.74"},{"key":"jsir.2012010102-11","doi-asserted-by":"crossref","unstructured":"Divina, F., & Aguilar-Ruiz, J. (2007). A multi-objective approach to discover biclusters in microarray data. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (pp. 385-392).","DOI":"10.1145\/1276958.1277038"},{"key":"jsir.2012010102-12","doi-asserted-by":"crossref","unstructured":"Gallo, C. A., Carballido, J. A., & Ponzoni, I. (2009). Microarray biclustering: A novel memetic approach based on the PISA platform. 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Biclustering of gene expression data by correlation-based scatter search. BioData Mining, 4(3).","DOI":"10.1186\/1756-0381-4-3"},{"key":"jsir.2012010102-20","doi-asserted-by":"crossref","unstructured":"Rathipriya, R., Thangavel, K., & Bagyamani, J. (2011). Binary particle swarm optimization based biclustering of web usage data. International Journal of Computers and their Applications, 25(2), 43-49.","DOI":"10.5120\/3001-4036"},{"key":"jsir.2012010102-21","first-page":"32","article-title":"Evolutionary biclustering of clickstream data.","volume":"8","author":"R.Rathipriya","year":"2011","journal-title":"International Journal of Computer Science Issues"},{"key":"jsir.2012010102-22","doi-asserted-by":"crossref","unstructured":"Xu, G., Zong, Y., Dolog, P., & Zhang, Y. (2010). Co-clustering analysis of weblogs using bipartite spectral projection approach. In R. Setchi, I. Jordanov, R. J. Howlett, & L. C. 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