[1] Michael Hahsler and Anthony R. Cassandra. Pomdp: A computational infrastructure for partially observable Markov decision processes. R Journal, 2024. Accepted for publication. [ DOI | preprint (PDF) | at the publisher ]
[2] Antônio Junior Alves Caiado and Michael Hahsler. AI content self-detection for transformer-based large language models. arXiv:2312.17289 [cs.CL], December 2023. [ DOI | at the publisher ]
[3] Michael Hahsler. ARULESPY: Exploring association rules and frequent itemsets in Python. arXiv:2305.15263 [cs.DB], May 2023. [ DOI | at the publisher ]
[4] M. Ledmi, C. Kara-Mohamed, M.E.H. Souidi, M. Hahsler, and Ledmi. Mining association rules for classification using frequent generator itemsets in arules package. International Journal of Data Mining, Modelling and Management, 15(2):203--221, 2023. [ DOI ]
[5] Michael Hahsler. recommenderlab: An R framework for developing and testing recommendation algorithms. arXiv:2205.12371 [cs.IR], May 2022. [ DOI | at the publisher ]
[6] Farzad Kamalzadeh, Vishal Ahuja, Michael Hahsler, and Michael E. Bowen. An analytics-driven approach for optimal individualized diabetes screening. Production and Operations Management, 30(9):3161--3191, September 2021. [ DOI | preprint (PDF) | at the publisher ]
[7] Zahra Gharibi and Michael Hahsler. A simulation-based optimization model to study the impact of multiple-region listing and information sharing on kidney transplant outcomes. International Journal of Environmental Research and Public Health, 18(3):873, January 2021. [ DOI | at the publisher ]
[8] Xinyi Ding, Zohreh Raziei, Eric C. Larson, Eli V. Olinick, Paul Krueger, and Michael Hahsler. Swapped face detection using deep learning and subjective assessment. EURASIP Journal on Information Security, 2020(6):1--12, May 2020. [ DOI ]
[9] Michael Hahsler and Anurag Nagar. rRDP: interface to the RDP classifier. Bioconductor version: Release (3.17), 2020. [ DOI | at the publisher ]
[10] Paul S. Krueger, Michael Hahsler, Eli V. Olinick, Sheila H. Williams, and Mohammadreza Zharfa. Quantitative classification of vortical flows based on topological features using graph matching. Proceedings of the Royal Society A, 475(2228):1--16, August 2019. [ DOI ]
[11] Michael Hahsler, Matthew Piekenbrock, and Derek Doran. dbscan: Fast density-based clustering with R. Journal of Statistical Software, 91(1):1--30, 2019. [ DOI ]
[12] Michael Hahsler, Ian Johnson, Tomas Kliegr, and Jaroslav Kuchar. Associative classification in R: arc, arulesCBA, and rCBA. R Journal, 11(2):254--267, 2019. [ DOI | preprint (PDF) | at the publisher ]
[13] Joan B B Soriano, Michael Hahsler, Cecilia Soriano, Cristina Martinez, Juan P de Torres, Jose M Marin, Pilar de Lucas, Borja G Cosio, Antonia Fuster, and Ciro Casanova. Temporal transitions in COPD severity stages within the GOLD 2017 classification system. Respiratory Medicine, 142:81--85, September 2018. [ DOI ]
[14] Usama El Shamy and Michael Hahsler. Data analytics applied to a microscale simulation model of soil liquefaction. In Geotechnical Earthquake Engineering and Soil Dynamics V. ASCE, June 2018. [ DOI ]
[15] Michael Hahsler and Anurag Nagar. Discovering patterns in gene ontology using association rule mining. Biostatistics and Biometrics Open Access Journal, 6(3):1--3, April 2018. [ DOI | preprint (PDF) ]
[16] Michael Hahsler. arulesViz: Interactive visualization of association rules with R. R Journal, 9(2):163--175, December 2017. [ DOI ]
[17] Zahra Gharibi, Mehmet Ayvaci, Michael Hahsler, Tracy Giacoma, Robert S. Gaston, and Bekir Tanriover. Cost-effectiveness of antibody-based induction therapy in deceased donor kidney transplantation in the United States. Transplantation, 101(6):1234--1241, June 2017. [ DOI | at the publisher ]
[18] Michael Hahsler, Matthew Bolaños, and John Forrest. stream: An extensible framework for data stream clustering research with R. Journal of Statistical Software, 76(14):1--52, February 2017. [ DOI | at the publisher ]
[19] Michael Hahsler. An experimental comparison of seriation methods for one-mode two-way data. European Journal of Operational Research, 257:133--143, February 2017. [ DOI | preprint (PDF) ]
[20] Jake Drew, Michael Hahsler, and Tyler Moore. Polymorphic malware detection using sequence classification methods. EURASIP Journal on Information Security, 2017(1):1--12, January 2017. [ DOI ]
[21] Michael Hahsler. Grouping association rules using lift. In C. Iyigun, R. Moghaddess, and A. Oztekin, editors, 11th INFORMS Workshop on Data Mining and Decision Analytics (DM-DA 2016), November 2016. [ preprint (PDF) ]
[22] Michael Hahsler and Matthew Bolaños. Clustering data streams based on shared density between micro-clusters. IEEE Transactions on Knowledge and Data Engineering, 28(6):1449--1461, June 2016. [ DOI | preprint (PDF) ]
[23] Jake Drew, Michael Hahsler, and Tyler Moore. Polymorphic malware detection using sequence classification methods. In International Workshop on Bio-inspired Security, Trust, Assurance and Resilience (BioSTAR 2016), May 2016. [ preprint (PDF) ]
[24] Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3):317--335, May 2016. [ DOI | at the publisher ]
[25] Becca Mokhtarpour, Jerrell T. Stracener, and Michael Hahsler. A data-analysis approach for improved decision-making in selecting the preferred SoS capability solution. In 2016 Conference on Systems Engineering Research, March 2016.
[26] Shaiba Hadil and Michael Hahsler. A comparison of machine learning methods for predicting tropical cyclone rapid intensification events. Research Journal of Applied Sciences, Engineering and Technology, 13(8):638--651, 2016. [ DOI ]
[27] Anurag Nagar, Michael Hahsler, and Hisham Al-Mubaid. Association rule mining of gene ontology annotation terms for SGD. In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, August 2015. [ DOI | preprint (PDF) ]
[28] Sudheer Chelluboina and Michael Hahsler. Trajectory segmentation using oblique envelopes. In 2015 IEEE International Conference on Information Reuse and Integration (IRI), pages 470--475. IEEE, August 2015. [ DOI ]
[29] Jörg Lässig and Michael Hahsler. Cooperative data analysis in supply chains using selective information disclosure. In Brian Borchers, J. Paul Brooks, and Laura McLay, editors, Operations Research and Computing: Algorithms and Software for Analytics, 14th INFORMS Computing Society Conference (ICS2015). INFORMS, January 2015. [ at the publisher ]
[30] Jake Drew and Michael Hahsler. Practical applications of locality sensitive hashing for unstructured data. In Proceedings of the 2014 CMG Conference: Performance and Capacity. CMG, November 2014. [ preprint (PDF) ]
[31] Jake Drew and Michael Hahsler. Strand: Fast sequence comparison using mapreduce and locality sensitive hashing. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Health Informatics (BCB 2014). ACM, September 2014. [ DOI | preprint (PDF) ]
[32] Matthew Bolaños, John Forrest, and Michael Hahsler. Clustering large datasets using data stream clustering techniques. In Myra Spiliopoulou, Lars Schmidt-Thieme, and Ruth Janning, editors, Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization, pages 135--143. Springer-Verlag, 2014. [ DOI | preprint (PDF) ]
[33] Hadil Shaiba and Michael Hahsler. An experimental comparison of different classifiers for predicting tropical cyclone rapid intensification events. In Proceedings of the International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME'2014), Dubai, UAE, January 2014. [ preprint (PDF) ]
[34] Hadil Shaiba and Michael Hahsler. Intensity prediction model for tropical cyclone rapid intensification events. In Proceedings of the IADIS Applied Computing 2013 (AC 2013) Conference, Fort Worth, TX, October 2013.
[35] Anurag Nagar and Michael Hahsler. Genomic sequence fragment identification using quasi-alignment. In Proceedings of the ACM BCB Conference 2013, Washington D.C., September 2013. [ DOI | preprint (PDF) ]
[36] Anurag Nagar and Michael Hahsler. Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment. BMC Bioinformatics, 14(Suppl. 11), 2013. [ DOI | at the publisher ]
[37] Anurag Nagar and Michael Hahsler. A novel quasi-alignment-based method for discovering conserved regions in genetic sequences. In Proceedings of the IEEE BIBM 2012 Workshop on Data-Mining of Next-Generation Sequencing. IEEE Computer Society Press, October 2012. [ DOI | preprint (PDF) ]
[38] Anurag Nagar and Michael Hahsler. Using text and data mining techniques to extract stock market sentiment from live news streams. In 2012 International Conference on Computer Technology and Science (ICCTS 2012), August 2012. [ preprint (PDF) ]
[39] Charlie Isaksson, Margaret H. Dunham, and Michael Hahsler. SOStream: Self organizing density-based clustering over data stream. In Petra Perner, editor, International Conference on Machine Learning and Data Mining (MLDM'2012), Lecture Notes in Computer Science LNAI 7376, pages 264--278. Springer, July 2012. [ DOI | preprint (PDF) ]
[40] Maya El Dayeh and Michael Hahsler. Biological pathway completion using network motifs and random walks on graphs. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2012), pages 229--236. IEEE, May 2012. [ preprint (PDF) ]
[41] Maya El Dayeh and Michael Hahsler. Analyzing incomplete biological pathways using network motifs. In 27th Symposium On Applied Computing (SAC 2012), volume 2, pages 1355--1360. ACM, 2012. [ preprint (PDF) ]
[42] Vladimir Jovanovic, Margaret H. Dunham, Michael Hahsler, and Yu Su. Evaluating hurricane intensity prediction techniques in real time. In Third IEEE ICDM Workshop on Knowledge Discovery from Climate Data, Proceedings of the of the 2011 IEEE International Conference on Data Mining Workshops (ICDMW 2011), pages 23--29. IEEE, December 2011. [ DOI | preprint (PDF) ]
[43] Michael Hahsler and Kurt Hornik. Dissimilarity plots: A visual exploration tool for partitional clustering. Journal of Computational and Graphical Statistics, 10(2):335--354, June 2011. [ DOI | preprint (PDF) ]
[44] Michael Hahsler and Sudheer Chelluboina. Visualizing association rules in hierarchical groups. Unpublished. Presented at the 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms (Interface 2011), June 2011. [ preprint (PDF) ]
[45] Michael Hahsler and Margaret H. Dunham. Temporal structure learning for clustering massive data streams in real-time. In SIAM Conference on Data Mining (SDM11), pages 664--675. SIAM, April 2011. [ DOI | preprint (PDF) ]
[46] Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977--1981, 2011. [ at the publisher ]
[47] Yu Su, Sudheer Chelluboina, Michael Hahsler, and Margaret H. Dunham. A new data mining model for hurricane intensity prediction. In Second IEEE ICDM Workshop on Knowledge Discovery from Climate Data: Prediction, Extremes and Impacts, Proceedings of the of the 2010 IEEE International Conference on Data Mining Workshops (ICDMW 2010), pages 98--105. IEEE, December 2010. [ DOI | preprint (PDF) ]
[48] Rao M. Kotamarti, Michael Hahsler, Douglas W. Raiford, and Margaret H. Dunham. Sequence transformation to a complex signature form for consistent phylogenetic tree using extensible Markov model. In Proceedings of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2010). IEEE, 2010. [ DOI | preprint (PDF) ]
[49] Michael Hahsler and Margaret H. Dunham. rEMM: Extensible Markov model for data stream clustering in R. Journal of Statistical Software, 35(5):1--31, 2010. [ DOI | at the publisher ]
[50] Rao M. Kotamarti, Michael Hahsler, Douglas Raiford, Monnie McGee, and Margaret H. Dunham. Analyzing taxonomic classification using extensible Markov models. Bioinformatics, 26(18):2235--2241, 2010. [ DOI | at the publisher ]
[51] Margaret H. Dunham, Michael Hahsler, and Myra Spiliopoulou, editors. Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques (StreamKDD'10). ACM Press, New York, NY, USA, 2010. [ at the publisher ]
[52] Margaret H. Dunham, Michael Hahsler, and Myra Spiliopoulou. Novel data stream pattern mining, Report on the StreamKDD'10 Workshop. SIGKDD Explorations, 12(2):54--55, 2010. [ at the publisher ]
[53] Rao M. Kotamarti, Douglas W. Raiford, Michael Hahsler, Yuhang Wang, Monnie McGee, and Margaret H. Dunham. Targeted genomic signature profiling with quasi-alignment statistics. Article 63, COBRA Preprint Series, November 2009. [ at the publisher ]
[54] Michael Hahsler and Kurt Hornik. Dissimilarity plots: A visual exploration tool for partitional clustering. Report 89, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, September 2009. [ at the publisher ]
[55] Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303--315, April 2008. [ DOI | preprint (PDF) ]
[56] Michael Hahsler, Kurt Hornik, and Christian Buchta. Getting things in order: An introduction to the R package seriation. Journal of Statistical Software, 25(3):1--34, March 2008. [ DOI | at the publisher ]
[57] Michael Hahsler and Kurt Hornik. TSP -- Infrastructure for the traveling salesperson problem. Journal of Statistical Software, 23(2):1--21, December 2007. [ DOI | at the publisher ]
[58] Michael Hahsler, Kurt Hornik, and Christian Buchta. Getting things in order: An introduction to the R package seriation. Report 58, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, August 2007. [ at the publisher ]
[59] Thomas Reutterer, Michael Hahsler, and Kurt Hornik. Data Mining und Marketing am Beispiel der explorativen Warenkorbanalyse. Marketing ZFP, 29(3):165--181, 2007. [ at the publisher ]
[60] Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5):437--455, 2007. [ DOI | preprint (PDF) | at the publisher ]
[61] Michael Hahsler and Kurt Hornik. Building on the arules infrastructure for analyzing transaction data with R. In R. Decker and H.-J. Lenz, editors, Advances in Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pages 449--456. Springer-Verlag, 2007. [ DOI | preprint (PDF) ]
[62] Christoph Breidert and Michael Hahsler. Adaptive conjoint analysis for pricing music downloads. In R. Decker and H.-J. Lenz, editors, Advances in Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pages 409--416. Springer-Verlag, 2007. [ DOI | preprint (PDF) ]
[63] Michael Hahsler and Kurt Hornik. TSP -- Infrastructure for the traveling salesperson problem. Report 45, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, December 2006. [ at the publisher ]
[64] Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2):137--166, September 2006. [ DOI | preprint (PDF) ]
[65] Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Report 38, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, August 2006. [ at the publisher ]
[66] Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization, pages 598--605. Springer-Verlag, 2006. [ preprint (PDF) | at the publisher ]
[67] Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Warenkorbanalyse mit Hilfe der Statistik-Software R. In Peter Schnedlitz, Renate Buber, Thomas Reutterer, Arnold Schuh, and Christoph Teller, editors, Innovationen in Marketing, pages 144--163. Linde-Verlag, 2006. [ preprint (PDF) ]
[68] Christoph Breidert, Michael Hahsler, and Thomas Reutterer. A review of methods for measuring willingness-to-pay. Innovative Marketing, 2(4):8--32, 2006. [ preprint (PDF) | at the publisher ]
[69] Michael Hahsler, Bettina Grün, and Kurt Hornik. arules -- A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15):1--25, October 2005. [ DOI ]
[70] Michael Hahsler, Bettina Grün, and Kurt Hornik. A computational environment for mining association rules and frequent item sets. Report 15, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, April 2005. [ at the publisher ]
[71] Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for rule mining. Report 14, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, March 2005. [ at the publisher ]
[72] Michael Hahsler and Stefan Koch. Discussion of a large-scale open source data collection methodology. In 38th Annual Hawaii International Conference on System Sciences (HICSS'05), January 3--6, 2005 Hilton Waikoloa Village, Big Island, Hawaii. IEEE Computer Society Press, 2005. [ preprint (PDF) | at the publisher ]
[73] Michael Hahsler. Optimizing web sites for customer retention. In Bing Liu, Myra Spiliopoulou, Jaideep Srivastava, and Alex Tuzhilin, editors, Proceedings of the 2005 International Workshop on Customer Relationship Management: Data Mining Meets Marketing, November 18--19, 2005, New York City, USA, 2005. [ preprint (PDF) ]
[74] Michael Hahsler. A quantitative study of the adoption of design patterns by open source software developers. In S. Koch, editor, Free/Open Source Software Development, pages 103--123. Idea Group Publishing, 2005. [ preprint (PDF) | at the publisher ]
[75] Georg Fessler, Michael Hahsler, and Michaela Putz. ePubWU -- Erfahrungen mit einer Volltext an der Wirtschaftsuniversität Wien. In Christian Enichlmayr, editor, Bibliotheken -- Fundament der Bildung, 28. Österreichischer Bibliothekartag 2004, Schriftenreihe der Oö. Landesbibliothek, pages 190--193, 2005.
[76] Christoph Breidert, Michael Hahsler, and Lars Schmidt-Thieme. Reservation price estimation by adaptive conjoint analysis. In Claus Weihs and Wolfgang Gaul, editors, Classification - the Ubiquitous Challenge, Studies in Classification, Data Analysis, and Knowledge Organization, pages 577--584. Springer-Verlag, 2005. [ preprint (PDF) | at the publisher ]
[77] Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Working Paper 07/2004, Working Papers on Information Processing and Information Management, Institut für Informationsverarbeitung und -wirtschaft, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, November 2004. [ at the publisher ]
[78] Susanne Hafner and Michael Hahsler. Preisvergleich zwischen Online-Shops und traditionellen Geschäften: Fallstudie Spieleeinzelhandel. Working Paper 04/2004, Working Papers on Information Processing and Information Management, Institut für Informationsverarbeitung und -wirtschaft, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, August 2004. [ at the publisher ]
[79] Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. Behavior-based recommender systems as value-added services for scientific libraries. In Hamparsum Bozdogan, editor, Statistical Data Mining & Knowledge Discovery, pages 433--454. Chapman & Hall / CRC, July 2004. [ preprint (PDF) ]
[80] Michael Hahsler and Stefan Koch. Cooperation and disruptive behaviour - learning from a multi-player internet gaming community. In Piet Kommers, Pedro Isaias, and Miguel Baptista Nunes, editors, IADIS International Conference Web Based Communities 2004, Lisbon, Portugal, 24--26 March 2004, pages 35--42. International Association for Development of the Information Society (IADIS), 2004. [ preprint (PDF) ]
[81] Georg Fessler, Michael Hahsler, Michaela Putz, Judith Schwarz, and Brigitta Wiebogen. Projektbericht ePubWU 2001--2003. Augasse 2--6, 1090 Wien, Wirtschaftsuniversität Wien, January 2004. [ preprint (PDF) ]
[82] Michael Hahsler. Integrating digital document acquisition into a university library: A case study of social and organizational challenges. Journal of Digital Information Management, 1(4):162--171, December 2003. [ preprint (PDF) ]
[83] Andreas Geyer-Schulz, Michael Hahsler, and Anke Thede. Comparing association-rules and repeat-buying based recommender systems in a B2B environment. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22--24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 421--429. Springer-Verlag, July 2003. [ at the publisher ]
[84] Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. An integration strategy for distributed recommender services in legacy library systems. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22--24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 412--420. Springer-Verlag, July 2003. [ at the publisher ]
[85] Michael Hahsler. A quantitative study of the application of design patterns in java. Working Paper 01/2003, Working Papers on Information Processing and Information Management, Institut für Informationsverarbeitung und -wirtschaft, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, January 2003. [ at the publisher ]
[86] Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. Recommenderdienste für wissenschaftliche Bibliotheken und Bibliotheksverbünde. In Andreas Geyer-Schulz and Alfred Taudes, editors, Informationswirtschaft: Ein Sektor mit Zukunft, Symposium 4.--5. September 2003, Wien, Österreich, Lecture Notes in Informatics (LNI) P-33, pages 43--58. Gesellschaft für Informatik, 2003. [ at the publisher ]
[87] Andreas Geyer-Schulz and Michael Hahsler. Comparing two recommender algorithms with the help of recommendations by peers. In O.R. Zaiane, J. Srivastava, M. Spiliopoulou, and B. Masand, editors, WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, Lecture Notes in Computer Science LNAI 2703, pages 137--158. Springer-Verlag, 2003. (Revised version of the WEBKDD 2002 paper “Evaluation of Recommender Algorithms for an Internet Information Broker based on Simple Association Rules and on the Repeat-Buying Theory”). [ preprint (PDF) | at the publisher ]
[88] Edward Bernroider, Michael Hahsler, Stefan Koch, and Volker Stix. Data Envelopment Analysis zur Unterstützung der Auswahl und Einführung von ERP-Systemen. In Andreas Geyer-Schulz and Alfred Taudes, editors, Informationswirtschaft: Ein Sektor mit Zukunft, Symposium 4.--5. September 2003, Wien, Österreich, Lecture Notes in Informatics (LNI) P-33, pages 11--26. Gesellschaft für Informatik, 2003. [ at the publisher ]
[89] Andreas Geyer-Schulz and Michael Hahsler. Software reuse with analysis patterns. In Proceedings of the 8th AMCIS, pages 1156--1165, Dallas, TX, August 2002. Association for Information Systems. [ preprint (PDF) ]
[90] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In R. Kohavi, B.M. Masand, M. Spiliopoulou, and J. Srivastava, editors, WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers, Lecture Notes in Computer Science LNAI 2356, pages 25--47. Springer-Verlag, July 2002. (Revised version of the WEBKDD 2001 paper “A Customer Purchase Incidence Model Applied to Recommender Systems”). [ preprint (PDF) | at the publisher ]
[91] Andreas Geyer-Schulz and Michael Hahsler. Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In Brij Masand, Myra Spiliopoulou, Jaideep Srivastava, and Osmar R. Zaiane, editors, Fourth WEBKDD Workshop: Web Mining for Usage Patterns & User Profiles, pages 100--114, Edmonton, Canada, July 2002. [ preprint (PDF) ]
[92] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Recommendations for virtual universities from observed user behavior. In W. Gaul and G. Ritter, editors, Classification, Automation, and New Media, Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15--17, 2000, pages 273--280. Springer-Verlag, 2002. [ preprint (PDF) | at the publisher ]
[93] Wolfgang Gaul, Andreas Geyer-Schulz, Michael Hahsler, and Lars Schmidt-Thieme. eMarketing mittels Recommendersystemen. Marketing ZFP, 24:47--55, 2002. [ at the publisher ]
[94] Walter Böhm, Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Repeat buying theory and its application for recommender services. In O. Opitz and M. Schwaiger, editors, Exploratory Data Analysis in Empirical Research, Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14--16, 2001, pages 229--239. Springer-Verlag, 2002. [ preprint (PDF) | at the publisher ]
[95] Andreas Geyer-Schulz and Michael Hahsler. Software engineering with analysis patterns. Working Paper 01/2001, Working Papers on Information Processing and Information Management, Institut für Informationsverarbeitung und -wirtschaft, Wirtschaftsuniversität Wien, Augasse 2--6, 1090 Wien, Austria, November 2001. [ at the publisher ]
[96] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Wissenschaftliche Recommendersysteme in Virtuellen Universitäten. In H.-J. Appelrath, R. Beyer, U. Marquardt, H.C. Mayr, and C. Steinberger, editors, Unternehmen Hochschule, Wien, Österreich, September 2001. Symposium UH2001, GI Lecture Notes in Informatics (LNI). [ preprint (PDF) | at the publisher ]
[97] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In WEBKDD2001 Workshop: Mining Log Data Across All Customer TouchPoints, pages 35--45, San Francisco, CA, August 2001. [ preprint (PDF) ]
[98] Michael Hahsler. Analyse Patterns im Softwareentwicklungsprozeß mit Beispielen für Informationsmanagement und deren Anwendungen für die Virtuellen Universität der Wirtschaftsuniversität Wien. Dissertation, Wirtschaftsuniversität Wien, Augasse 2--6, A 1090 Wien, Österreich, January 2001. [ preprint (PDF) | at the publisher ]
[99] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Educational and scientific recommender systems: Designing the information channels of the virtual university. International Journal of Engineering Education, 17(2):153--163, 2001. [ preprint (PDF) ]
[100] Michael Hahsler and Bernd Simon. User-centered navigation re-design for web-based information systems. In H. Michael Chung, editor, Proceedings of the Sixth Americas Conference on Information Systems (AMCIS 2000), pages 192--198, Long Beach, CA, 2000. Association for Information Systems. [ preprint (PDF) ]
[101] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. myvu: A next generation recommender system based on observed consumer behavior and interactive evolutionary algorithms. In Wolfgang Gaul, Otto Opitz, and Martin Schader, editors, Data Analysis: Scientific Modeling and Practical Applications, Studies in Classification, Data Analysis, and Knowledge Organization, pages 447--457. Springer Verlag, Heidelberg, Germany, 2000. [ preprint (PDF) | at the publisher ]
[102] Andreas Geyer-Schulz and Michael Hahsler. Lebenslanges virtuelles Lernen. In Franciszek Grucza, editor, Europas Arbeitswelt von Morgen, pages 51--54, Wien, 2000. Wiener Zentrum der Polnischen Akademie der Wissenschaften.
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