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Michael Hahsler
Michael Hahsler
Associate Professor, Computer Science, Southern Methodist University
Zweryfikowany adres z lyle.smu.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
A review of methods for measuring willingness-to-pay
C Breidert, M Hahsler, T Reutterer
Innovative Marketing 2 (4), 8-32, 2006
10342006
dbscan: Fast Density-based Clustering with R
M Hahsler, M Piekenbrock, D Doran
Journal of Statistical Software 91 (1), 1-30, 2019
8742019
A computational environment for mining association rules and frequent item sets
M Hahsler, B Grün, K Hornik
Journal of Statistical Software 14 (15), 1-25, 2005
7642005
Getting things in order: an introduction to the R package seriation
M Hahsler, K Hornik, C Buchta
Journal of Statistical Software 25 (3), 1-34, 2008
3182008
arules: Mining Association Rules and Frequent Itemsets
M Hahsler, C Buchta, B Gruen, K Hornik
Comprehensive R Archive Network, 2005
313*2005
TSP-Infrastructure for the traveling salesperson problem
M Hahsler, K Hornik
Journal of Statistical Software 23 (2), 1-21, 2007
2902007
The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets
M Hahsler, S Chelluboina, K Hornik, C Buchta
The Journal of Machine Learning Research 12, 2021-2025, 2011
2072011
arulesViz: Visualizing Association Rules and Frequent Itemsets
M Hahsler, S Chelluboina
Comprehensive R Archive Network, 2011
206*2011
Visualizing association rules in hierarchical groups
M Hahsler, R Karpienko
Journal of Business Economics 87 (3), 317-335, 2017
1972017
Clustering data streams based on shared density between micro-clusters
M Hahsler, M Bolanos
IEEE Transactions on Knowledge and Data Engineering 99 (99), 1-14, 2016
1872016
recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms
M Hahsler
arXiv preprint arXiv:2205.12371, 2022
1232022
Density-based clustering of applications with noise (DBSCAN) and related algorithms
M Hahsler, M Piekenbrock
Comprehensive R Archive Network, 2019
116*2019
A probabilistic comparison of commonly used interest measures for association rules
M Hahsler
https://mhahsler.github.io/arules/docs/measures, 2015
105*2015
arulesViz: interactive visualization of association rules with R
M Hahsler
The R Journal 9 (2), 2017
962017
SOStream: Self organizing density-based clustering over data stream
C Isaksson, MH Dunham, M Hahsler
International workshop on machine learning and data mining in pattern …, 2012
952012
New probabilistic interest measures for association rules
M Hahsler, K Hornik
Intelligent Data Analysis 11 (5), 437-455, 2007
922007
Implications of probabilistic data modeling for mining association rules
M Hahsler, K Hornik, T Reutterer
From Data and Information Analysis to Knowledge Engineering: Proceedings of …, 2006
862006
Swapped face detection using deep learning and subjective assessment
X Ding, Z Raziei, EC Larson, EV Olinick, P Krueger, M Hahsler
EURASIP Journal on Information Security 2020, 1-12, 2020
802020
Polymorphic malware detection using sequence classification methods
J Drew, T Moore, M Hahsler
2016 IEEE Security and Privacy Workshops (SPW), 81-87, 2016
792016
Polymorphic malware detection using sequence classification methods and ensembles: BioSTAR 2016 Recommended Submission-EURASIP Journal on Information Security
J Drew, M Hahsler, T Moore
EURASIP Journal on Information Security 2017, 1-12, 2017
762017
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