{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T07:21:57Z","timestamp":1725866517491},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319471594"},{"type":"electronic","value":"9783319471600"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-47160-0_1","type":"book-chapter","created":{"date-parts":[[2016,9,28]],"date-time":"2016-09-28T03:43:07Z","timestamp":1475034187000},"page":"3-22","source":"Crossref","is-referenced-by-count":3,"title":["Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering"],"prefix":"10.1007","author":[{"given":"Pawan","family":"Lingras","sequence":"first","affiliation":[]},{"given":"Matt","family":"Triff","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,9,29]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Ammar, A., Elouedi, Z., Lingras, P.: Decremental possibilistic k-modes. In: SCAI, pp. 15\u201324 (2013)","DOI":"10.1007\/978-3-642-44949-9_2"},{"key":"1_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-642-44949-9_2","volume-title":"Multi-disciplinary Trends in Artificial Intelligence","author":"A Ammar","year":"2013","unstructured":"Ammar, A., Elouedi, Z., Lingras, P.: Incremental rough possibilistic k-modes. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds.) MIWAI 2013. LNCS (LNAI), vol. 8271, pp. 13\u201324. Springer, Heidelberg (2013). doi: 10.1007\/978-3-642-44949-9_2"},{"key":"1_CR3","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1086\/260062","volume":"81","author":"F Black","year":"1973","unstructured":"Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81, 637\u2013654 (1973)","journal-title":"J. Polit. Econ."},{"key":"1_CR4","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/01969727308546046","volume":"3","author":"JC Dunn","year":"1973","unstructured":"Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32\u201357 (1973)","journal-title":"J. Cybern."},{"key":"1_CR5","unstructured":"Eagle, N.: The reality mining data (2010). http:\/\/eprom.mit.edu\/data\/RealityMining_ReadMe.pdf"},{"issue":"1","key":"1_CR6","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100\u2013108 (1979). http:\/\/www.jstor.org\/stable\/2346830","journal-title":"J. R. Stat. Soc. Ser. C (Appl. Stat.)"},{"issue":"3","key":"1_CR7","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1080\/08839514.2014.883902","volume":"28","author":"A Janusz","year":"2014","unstructured":"Janusz, A., \u015alezak, D.: Rough set methods for attribute clustering and selection. Appl. Artif. Intell. 28(3), 220\u2013242 (2014)","journal-title":"Appl. Artif. Intell."},{"key":"1_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/978-3-642-11164-8_101","volume-title":"Pattern Recognition and Machine Intelligence","author":"M Joshi","year":"2009","unstructured":"Joshi, M., Lingras, P.: Evolutionary and iterative crisp and rough clustering ii: experiments. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 621\u2013627. Springer, Heidelberg (2009). doi: 10.1007\/978-3-642-11164-8_101"},{"issue":"3","key":"1_CR9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1023\/A:1011219918340","volume":"16","author":"P Lingras","year":"2001","unstructured":"Lingras, P.: Unsupervised rough set classification using gas. J. Intell. Inf. Syst. 16(3), 215\u2013228 (2001)","journal-title":"J. Intell. Inf. Syst."},{"key":"1_CR10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ins.2013.09.018","volume":"257","author":"P Lingras","year":"2013","unstructured":"Lingras, P., Elagamy, A., Ammar, A., Elouedi, Z.: Iterative meta-clustering through granular hierarchy of supermarket customers and products. Inf. Sci. 257, 14\u201331 (2013)","journal-title":"Inf. Sci."},{"key":"1_CR11","unstructured":"Lingras, P., Haider, F.: Rough ensemble clustering. In: Intelligent Data Analysis, Special Issue on Business Analytics in Finance and Industry (2014)"},{"issue":"1","key":"1_CR12","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s41066-015-0007-9","volume":"1","author":"P Lingras","year":"2016","unstructured":"Lingras, P., Haider, F., Triff, M.: Granular meta-clustering based on hierarchical, network, and temporal connections. Granular Comput. 1(1), 71\u201392 (2016)","journal-title":"Granular Comput."},{"issue":"4","key":"1_CR13","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/TFUZZ.2014.2349532","volume":"23","author":"P Lingras","year":"2015","unstructured":"Lingras, P., Triff, M.: Fuzzy and crisp recursive profiling of online reviewers and businesses. IEEE Trans. Fuzzy Syst. 23(4), 1242\u20131258 (2015)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"1","key":"1_CR14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/B:JIIS.0000029668.88665.1a","volume":"23","author":"P Lingras","year":"2004","unstructured":"Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Syst. 23(1), 5\u201316 (2004)","journal-title":"J. Intell. Inf. Syst."},{"issue":"12","key":"1_CR15","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1016\/j.patrec.2004.05.007","volume":"25","author":"S Mitra","year":"2004","unstructured":"Mitra, S.: An evolutionary rough partitive clustering. Pattern Recogn. Lett. 25(12), 1439\u20131449 (2004)","journal-title":"Pattern Recogn. Lett."},{"issue":"8","key":"1_CR16","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1016\/j.patcog.2006.02.002","volume":"39","author":"G Peters","year":"2006","unstructured":"Peters, G.: Some refinements of rough k-means clustering. Pattern Recogn. 39(8), 1481\u20131491 (2006)","journal-title":"Pattern Recogn."},{"issue":"2","key":"1_CR17","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.ijar.2012.10.003","volume":"54","author":"G Peters","year":"2013","unstructured":"Peters, G., Crespo, F., Lingras, P., Weber, R.: Soft clustering-fuzzy and rough approaches and their extensions and derivatives. Int. J. Approximate Reasoning 54(2), 307\u2013322 (2013)","journal-title":"Int. J. Approximate Reasoning"},{"issue":"10","key":"1_CR18","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1016\/j.asoc.2012.05.015","volume":"12","author":"G Peters","year":"2012","unstructured":"Peters, G., Weber, R., Nowatzke, R.: Dynamic rough clustering and its applications. Appl. Soft Comput. 12(10), 3193\u20133207 (2012)","journal-title":"Appl. Soft Comput."},{"key":"1_CR19","first-page":"13","volume":"815","author":"SC Sharma","year":"1981","unstructured":"Sharma, S.C., Werner, A.: Improved method of grouping provincewide permanent traffic counters. Transp. Res. Rec. 815, 13\u201318 (1981)","journal-title":"Transp. Res. Rec."},{"issue":"1","key":"1_CR20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/2522968.2522981","volume":"46","author":"JA Silva","year":"2013","unstructured":"Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho, A.C., Gama, J.: Data stream clustering: a survey. ACM Comput. Surv. (CSUR) 46(1), 13 (2013)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Slezak, D.: Rough sets and few-objects-many-attributes problem: the case study of analysis of gene expression data sets. In: Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007, pp. 437\u2013442. IEEE (2007)","DOI":"10.1109\/FBIT.2007.160"},{"key":"1_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-642-10509-8_3","volume-title":"Future Generation Information Technology","author":"D \u015al\u0119zak","year":"2009","unstructured":"\u015al\u0119zak, D., Kowalski, M.: Intelligent data granulation on load: improving infobright\u2019s knowledge grid. In: Lee, Y., Kim, T., Fang, W., \u015al\u0119zak, D. (eds.) FGIT 2009. LNCS, vol. 5899, pp. 12\u201325. Springer, Heidelberg (2009). doi: 10.1007\/978-3-642-10509-8_3"},{"issue":"1\u20134","key":"1_CR23","doi-asserted-by":"crossref","first-page":"445","DOI":"10.3233\/FI-2013-920","volume":"127","author":"D \u015alezak","year":"2013","unstructured":"\u015alezak, D., Synak, P., Wojna, A., Wr\u00f3blewski, J.: Two database related interpretations of rough approximations: data organization and query execution. Fundamenta Informaticae 127(1\u20134), 445\u2013459 (2013)","journal-title":"Fundamenta Informaticae"},{"key":"1_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/978-3-642-10646-0_48","volume-title":"Rough Sets, Fuzzy Sets, Data Mining and Granular Computing","author":"Y Yao","year":"2009","unstructured":"Yao, Y., Lingras, P., Wang, R., Miao, D.: Interval set cluster analysis: a re-formulation. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., \u015al\u0119zak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 398\u2013405. Springer, Heidelberg (2009). doi: 10.1007\/978-3-642-10646-0_48"},{"key":"1_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/978-3-319-11740-9_70","volume-title":"Rough Sets and Knowledge Technology","author":"H Yu","year":"2014","unstructured":"Yu, H., Su, T., Zeng, X.: A three-way decisions clustering algorithm for incomplete data. In: Miao, D., Pedrycz, W., \u015al\u0229zak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS (LNAI), vol. 8818, pp. 765\u2013776. Springer, Heidelberg (2014). doi: 10.1007\/978-3-319-11740-9_70"},{"key":"1_CR26","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.knosys.2015.05.028","volume":"91","author":"H Yu","year":"2016","unstructured":"Yu, H., Zhang, C., Wang, G.: A tree-based incremental overlapping clustering method using the three-way decision theory. Knowl.-Based Syst. 91, 189\u2013203 (2016)","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"1_CR27","first-page":"147","volume":"20","author":"P Zhang","year":"2011","unstructured":"Zhang, P., Joshi, M., Lingras, P.: Use of stability and seasonality analysis for optimal inventory prediction models. J. Intell. Syst. 20(2), 147\u2013166 (2011)","journal-title":"J. Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-47160-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,26]],"date-time":"2020-09-26T08:40:02Z","timestamp":1601109602000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-47160-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319471594","9783319471600"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-47160-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}