Michael Hahsler

Anurag Nagar, Michael Hahsler: A novel quasi-alignment-based method for discovering conserved regions in genetic sequences. BIBM Workshops 2012: 662-669

Maya El Dayeh, Michael Hahsler: Biological pathway completion using network motifs and random walks on graphs. CIBCB 2012: 229-236

Charlie Isaksson, Margaret H. Dunham, Michael Hahsler: SOStream: Self Organizing Density-Based Clustering over Data Stream. MLDM 2012: 264-278

Maya El Dayeh, Michael Hahsler: Analyzing incomplete biological pathways using network motifs. SAC 2012: 1355-1360

Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta: The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets. Journal of Machine Learning Research 12: 2021-2025 (2011)

Vladimir Jovanovic, Margaret H. Dunham, Michael Hahsler, Yu Su: Evaluating Hurricane Intensity Prediction Techniques in Real Time. ICDM Workshops 2011: 23-29

Michael Hahsler, Margaret H. Dunham: Temporal Structure Learning for Clustering Massive Data Streams in Real-Time. SDM 2011: 664-675

Rao M. Kotamarti, Michael Hahsler, Douglas Raiford, Monnie McGee, Margaret H. Dunham: Analyzing taxonomic classification using extensible Markov models. Bioinformatics 26(18): 2235-2241 (2010)

Margaret H. Dunham, Michael Hahsler, Myra Spiliopoulou: Novel data stream pattern mining report on the StreamKDD'10 workshop. SIGKDD Explorations 12(2): 54-55 (2010)

Rao M. Kotamarti, Michael Hahsler, Douglas W. Raiford, Margaret H. Dunham: Sequence transformation to a complex signature form for consistent phylogenetic tree using Extensible Markov Model. CIBCB 2010: 1-8

Yu Su, Sudheer Chelluboina, Michael Hahsler, Margaret H. Dunham: A New Data Mining Model for Hurricane Intensity Prediction. ICDM Workshops 2010: 98-105

Michael Hahsler, Christian Buchta, Kurt Hornik: Selective association rule generation. CoRR abs/0803.0954 (2008)

Michael Hahsler, Kurt Hornik: New probabilistic interest measures for association rules. CoRR abs/0803.0966 (2008)

Michael Hahsler: Optimizing Web Sites for Customer Retention. CoRR abs/0803.1104 (2008)

Michael Hahsler: A Model-Based Frequency Constraint for Mining Associations from Transaction Data. CoRR abs/0803.3224 (2008)

Michael Hahsler, Kurt Hornik: New probabilistic interest measures for association rules. Intell. Data Anal. 11(5): 437-455 (2007)

Michael Hahsler: A Model-Based Frequency Constraint for Mining Associations from Transaction Data. Data Min. Knowl. Discov. 13(2): 137-166 (2006)

Christoph Breidert, Michael Hahsler: Adaptive Conjoint Analysis for Pricing Music Downloads. GfKl 2006: 409-416

Michael Hahsler, Kurt Hornik: Building on the Arules Infrastructure for Analyzing Transaction Data with R. GfKl 2006: 449-456

Michael Hahsler, Kurt Hornik, Thomas Reutterer: Implications of Probabilistic Data Modeling for Mining Association Rules. GfKl 2005: 598-605

Michael Hahsler, Stefan Koch: Discussion of a Large-Scale Open Source Data Collection Methodology. HICSS 2005

Christoph Breidert, Michael Hahsler, Lars Schmidt-Thieme: Reservation Price Estimation by Adaptive Conjoint Analysis. GfKl 2004: 569-576

Michael Hahsler: Integrating Digital Document Acquisition into a University Library: A Case Study of Social and Organizational Challenges. JDIM 1(4): 162-171 (2003)

Edward Bernroider, Michael Hahsler, Stefan Koch, Volker Stix: Data Envelopment Analysis zur Unterstützung der Auswahl und Einführung von ERP-Systemen. Informationswirtschaft: Ein Sektor mit Zukunft 2003: 11-26

Andreas Geyer-Schulz, Michael Hahsler, Andreas W. Neumann, Anke Thede: Recommenderdienste für wissenschaftliche Bibliotheken und Bibliotheksverbünde. Informationswirtschaft: Ein Sektor mit Zukunft 2003: 43-57

Andreas Geyer-Schulz, Michael Hahsler: Comparing Two Recommender Algorithms with the Help of Recommendations by Peers. WEBKDD 2002: 137-158

Andreas Geyer-Schulz, Michael Hahsler, Maximillian Jahn: A Customer Purchase Incidence Model Applied to Recommender Services. WEBKDD 2001: 25-47

Andreas Geyer-Schulz, Michael Hahsler, Maximillian Jahn: Wissenschaftliche Recommendersysteme in Virtuellen Universitäten. UH 2001: 101-114