<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>e6e0c610-8fd4-4db4-b862-1f7317cdeec9</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.G8905.0510721</doi>
<citation_list><citation key="ref0"><unstructured_citation>Fayyad, U. M., G. Piatetsky-Shapiro, P. Smyth, and Ft. Uthurusamy, 1996. Advances in Knowledge Discovery and Data Mining, (AKDDM), AAAI/MIT Press.</unstructured_citation></citation><citation key="ref1"><unstructured_citation>Fayyad, U.M., Haussler, D. and Stolorz, Z. 1996. KDD for Science Data Analysis; Issues and Examples. Proc. 2nd Int. Conj. on Knowledge Discovery and Data Mining (KDD-96), Menlo Park, CA: AAAI Press.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>Fayyad, U.M., Piatetsky-Shapiro, G., and Smyth, P. 1996. From Data Mining to Knowledge Discovery: An Overview, in AI(DDM, AAAI/MIT Press, pp. 1-30</unstructured_citation></citation><citation key="ref3"><doi>10.1007/978-0-387-35503-0_28</doi><unstructured_citation>D. A. Swayne, R. Denzer, L. Lilburne, M. Purvis, N. W. T. Quinn, and A. Storey, &quot;?,&quot; in Environmental Software Systems, vol. 39, R. Denzer, D. A. Swayne, M. Purvis, and G. Schimak, Eds. Boston, MA: Springer US, 2000, pp. 259-268.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>U. Baizyldayeva, O. K. Vlasov, A. A. Kuandykov, and T. B. Akhmetov, &quot;Multi-Criteria Decision Support Systems. Comparative Analysis,&quot; 2013.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>Shearer, C., The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13-22, 2000.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>Adriaans. P and Zantinge.D, Data mining, Addison-Wesley, 1999.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>Berry, M. J., &amp; Gordon, L., Data mining techniques: For marketing, sales, and customer support. New York, NY: Wiley, 1997.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>SAS Enterprise Miner - SEMMA. SAS Institute. Accessed from http://www.sas.com/technologies/analytics/datamining/miner/semma.html, on May 2008</unstructured_citation></citation><citation key="ref9"><unstructured_citation>Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., &amp; Zanasi, A., Discovering data mining: From concept to implementation. Upper Saddle River, NJ: Prentice Hall, 1998.</unstructured_citation></citation><citation key="ref10"><doi>10.1145/379300.379323</doi><unstructured_citation>Hirji, K. K., Exploring data mining implementation. Communications of the ACM, 44(7), 87-93. doi:10.1145/379300.379323, 2001.</unstructured_citation></citation><citation key="ref11"><doi>10.1145/221270.221321</doi><unstructured_citation>Anand, S. S., Bell, D. A., &amp; Hughes, J. G., The role of domain knowledge in data mining. In Proceedings of the 4th International Conference on Information and Knowledge Management (pp. 37-43), 1995.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>Anand, S.S., Büchner, A.G., Decision Support through Data Mining, FT Pitman Publishers, 1998.</unstructured_citation></citation><citation key="ref13"><doi>10.1007/s003660070009</doi><unstructured_citation>Buchheit, RB, Garrett, JH, Jr, Lee, SR and Brahme, R, A knowledge discovery framework for civil infrastructure: a case study of the intelligent workplace. Engineering with Computers 16(3-4), 264-274, 2000.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>Jensen, S., Mining medical data for predictive and sequential patterns: PKDD 2001. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2001 Discovery Challenge on Thrombosis Data, 2001.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>Butler, S., An investigation into the relative abilities of three alternative data mining methods to derive information of business value from retail store-based transaction data. BSc thesis, School of Computing and Mathematics, Deakin University, Australia, 2002.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>Blockeel, H. and Moyle, S., Collaborative data mining needs centralized model evaluation. In Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned, pp.21-28, 2002.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>Silva, E.M., Do Prado, H.A. and Ferneda, E., Text mining: crossing the chasm between the academy and the industry. Management Information Systems 6, 351-361, 2002.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>Jensen, S., Mining medical data for predictive and sequential patterns: PKDD 2001. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD2001 Discovery Challenge on Thrombosis Data, 2001.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>Butler, S., An investigation into the relative abilities of three alternative data mining methods to derive information of business value from retail store-based transaction data. BSc thesis, School of Computing and Mathematics, Deakin University, Australia, 2002.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>Blockeel, H. and Moyle, S., Collaborative data mining needs centralized model evaluation. In Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned, pp.21-28, 2002.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>Silva, EM, Do Prado, HA and Ferneda, E., Text mining: crossing the chasm between the academy and the industry. Management Information Systems 6, 351-361, 2002.</unstructured_citation></citation><citation key="ref22"><doi>10.1007/978-3-540-46652-9_4</doi><unstructured_citation>Hipp, J and Lindner, G., Analyzing warranty claims of automobiles. An application description following the CRISP-DM data mining process. In Proceedings of 5th International Computer Science Conference, Hong Kong, China, pp.31-40, 1999.</unstructured_citation></citation><citation key="ref23"><doi>10.1145/347090.347174</doi><unstructured_citation>Gersten, W., Wirth, R. and Arndt D., Predictive modeling in automotive direct marketing: tools, experiences and open issues. In Proceeding of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 398-406, 2000.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>Moyle, S., Bohanec, M. and Ostrowski, E., Large and tall buildings: a case study in the application of decision support and data mining. In Proceedings of the ECML/PKDD'02 workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, pp.88-99, 2002.</unstructured_citation></citation><citation key="ref25"><doi>10.1016/j.eswa.2004.05.015</doi><unstructured_citation>Li, S-T and Shue, L-Y, Data mining to aid policy making in air pollution management. Expert Systems with Applications 27(3), 331-340, 2004.</unstructured_citation></citation><citation key="ref26"><doi>10.1145/1014052.1016917</doi><unstructured_citation>De Abajo, N, Lobato, V, Diez, AB and Cuesta, SR., ANN quality diagnostic models for packaging manufacturing: an industrial Data Mining case study. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 799-804, 2004.</unstructured_citation></citation><citation key="ref27"><doi>10.1109/51.853478</doi><unstructured_citation>Cios, K, Teresinska, A, Konieczna, S, Potocka, J and Sharma, S., Diagnosing myocardial perfusion from PECT bull's-eye maps-a knowledge discovery approach. IEEE Engineering in Medicine and Biology Magazine, Special issue on Medical Data Mining and Knowledge Discovery 19(4), 17-25, 2000.</unstructured_citation></citation><citation key="ref28"><doi>10.1007/1-84628-183-0_1</doi><unstructured_citation>Cios, K and Kurgan, L, Trends in data mining and knowledge discovery. In Pal, N and Jain, L (eds) Advanced Techniques in Knowledge Discovery and Data Mining. Springer, pp.1-26, 2005.</unstructured_citation></citation><citation key="ref29"><doi>10.1109/51.853485</doi><unstructured_citation>Sacha, J, Cios, K and Goodenday, L., Issues in automating cardiac SPECT diagnosis. IEEE Engineering in Medicine and Biology Magazine, Special issue on Medical Data Mining and Knowledge Discovery 19(4), 78-88, 2000.</unstructured_citation></citation><citation key="ref30"><doi>10.1016/S0933-3657(01)00082-3</doi><unstructured_citation>Kurgan, L, Cios, K, Tadeusiewicz, R, Ogiela, M and Goodenday, L., Knowledge discovery approach to automated cardiac SPECT diagnosis. Artiﬁcial Intelligence in Medicine 23(2), 149-169, 2001.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>Goh, KG, Hsu, W, Lee, ML and Wang, H., ADRIS: an automatic diabetic retinal image screening system. In Cios, K (ed.) Medical Data Mining and Knowledge Discovery, pp. 181-207, 2001.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>Shalvi, D and DeClaris, N., A data clustering and visualization methodology for epidemiological pathology discoveries. In Cios, K (ed.) Medical Data Mining and Knowledge Discovery, pp. 129-151, 2001.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>Cios, K (ed.) 2001, Medical Data Mining and Knowledge Discovery. Springer-Verlag.</unstructured_citation></citation><citation key="ref34"><doi>10.1016/S0933-3657(02)00054-4</doi><unstructured_citation>Maruster, L, Weijters, T, De Vries, G, Van den Bosch, A and Daelemans, W, 2002, Logistic-based patient grouping for multi-disciplinary treatment. Artiﬁcial Intelligence in Medicine 26(1-2), 87-107.</unstructured_citation></citation><citation key="ref35"><doi>10.1016/S0933-3657(02)00053-2</doi><unstructured_citation>Ganzert, S. Guttmann, J, Kersting, K, Kuhlen, R, Putensen, C, Sydow, M and Kramer, S., Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning. Artiﬁcial Intelligence in Medicine 26(1-2), 69-86, 2002.</unstructured_citation></citation><citation key="ref36"><doi>10.1016/S0933-3657(02)00057-X</doi><unstructured_citation>Perner, P., Perner, H. and Muller, B., Mining knowledge for HEp-2 cell image classiﬁcation. Artiﬁcial Intelligence in Medicine 26(1-2), 161-173, 2002.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>Hofer, J. and Brezany P., Distributed Decision Tree Induction within the Grid Data Mining Framework GridMiner-Core. GridMiner TR2004-04, Institute for Software Science, University of Vienna, 2004.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>Kurgan, L, Cios, K, Sontag, M and Accurso, F., Mining the cystic ﬁbrosis data. In Zurada, J and Kantardzic, M (eds) Next Generation of Data-Mining Applications. IEEE Press and Wiley, pp. 415-444, 2005.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>Han, J. and Kamber, M., Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>Edelstein, H., Data mining: let's get practical. DB2 Magazine 3(2), summer, 1998.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>Klosgen, W and Zytkow, J, 2002, The knowledge discovery process. In Klosgen, W and Zytkow, J (eds) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, pp.10-21.</unstructured_citation></citation><citation key="ref42"><doi>10.2481/dsj.4.39</doi><unstructured_citation>Haglin, D, Roiger, R, Hakkila, J and Giblin, T, 2005, A tool for public analysis of scientiﬁc data. Data Science Journal 4(30), 39-53.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>Haagsma I.G. and Johanns R.D., &quot;Decision support systems: An integrated approach,&quot; in Environmental Systems, edited by P. Zannetti, vol. II, pp. 205-212, 1994.</unstructured_citation></citation><citation key="ref44"><doi>10.1016/S1364-8152(98)00002-4</doi><unstructured_citation>Gabaldo ́n C., Ferrer J., Seco A., and Marzal P., &quot;A soft- ware for the integrated design of wastewater treatment plants,&quot; Environmental Modelling and Software, vol. 13, no. 1, pp. 31- 44, 1998.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>Guariso G. and Page B. (Eds.), &quot;Computers support for environmental impact assessment,&quot; in IFIP, North-Holland, ISBN 0-444-81838-3, 1994.</unstructured_citation></citation><citation key="ref46"><doi>10.2166/wst.1994.0041</doi><unstructured_citation>Okubo T., Kubo K., Hosomi M., and Murakami A., &quot;A knowledge-based decision support system for selecting small- scale wastewater treatment processes,&quot; Water Science Technol- ogy, vol. 30, no. 2, pp. 175-184, 1994.</unstructured_citation></citation><citation key="ref47"><doi>10.1016/0967-0661(93)91624-6</doi><unstructured_citation>Serra P., Lafuente J., Moreno R., de Prada C., and Poch M., &quot;Development of a real-time expert system for wastewater treatment plants control,&quot; Control. Eng. Practice, vol. 1, no. 2, pp. 329-335, 1993.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>Aarts R.J., Knowledge-based Systems for Bioprocesses, Tech- nical Research Centre of Finland, vol. 120, 1992.</unstructured_citation></citation><citation key="ref49"><journal_title>Expert Systems</journal_title><author>Fox</author><volume>1</volume><issue>1</issue><first_page>25</first_page><cYear>1984</cYear><doi>10.1111/j.1468-0394.1984.tb00424.x</doi><article_title>ISIS?a knowledge-based system for factory scheduling</article_title><unstructured_citation>Fox, M. S., &amp; Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49.</unstructured_citation></citation><citation key="ref50"><journal_title>In Exploring Intelligent Decision Support Systems (pp</journal_title><author>Mar-Ortiz</author><cYear>2018</cYear><doi>10.1007/978-3-319-74002-7_3</doi><article_title>Challenges in the Design of Decision Support Systems for Port and Maritime Supply Chains</article_title><unstructured_citation>Mar-Ortiz, J., Gracia, M. D., &amp; Castillo-García, N. (2018). Challenges in the Design of Decision Support Systems for Port and Maritime Supply Chains. In Exploring Intelligent Decision Support Systems (pp. 49-71). Springer, Cham.</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
