Kiri Wagstaff
Kiri Wagstaff
Jet Propulsion Laboratory
Verified email at jpl.nasa.gov - Homepage
Title
Cited by
Cited by
Year
Constrained k-means clustering with background knowledge
K Wagstaff, C Cardie, S Rogers, S Schrödl
Icml 1, 577-584, 2001
33272001
Clustering with instance-level constraints
K Wagstaff, C Cardie
AAAI/IAAI 1097, 577-584, 2000
8112000
Constrained clustering: Advances in algorithms, theory, and applications
S Basu, I Davidson, K Wagstaff
CRC Press, 2008
6562008
Noun phrase coreference as clustering
C Cardie, K Wagstaff
1999 Joint SIGDAT Conference on Empirical Methods in Natural Language …, 1999
2991999
Mining GPS traces for map refinement
S Schroedl, K Wagstaff, S Rogers, P Langley, C Wilson
Data mining and knowledge Discovery 9 (1), 59-87, 2004
2842004
Machine learning that matters
K Wagstaff
arXiv preprint arXiv:1206.4656, 2012
2692012
Measuring constraint-set utility for partitional clustering algorithms
I Davidson, KL Wagstaff, S Basu
European conference on principles of data mining and knowledge discovery …, 2006
2612006
Multidocument summarization via information extraction
M White, T Korelsky, C Cardie, V Ng, D Pierce, K Wagstaff
Proceedings of the first international conference on Human language …, 2001
1392001
Intelligent clustering with instance-level constraints
KL Wagstaff
Cornell University, 2002
1282002
The commensal real-time ASKAP fast-transients (CRAFT) survey
JP Macquart, M Bailes, NDR Bhat, GC Bower, JD Bunton, S Chatterjee, ...
Publications of the Astronomical Society of Australia 27 (3), 272-282, 2010
1112010
VAST: an ASKAP survey for variables and slow transients
T Murphy, S Chatterjee, DL Kaplan, J Banyer, ME Bell, HE Bignall, ...
Publications of the Astronomical Society of Australia 30, 2013
1022013
Alpha seeding for support vector machines
D DeCoste, K Wagstaff
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
1012000
Machine learning for science and society
C Rudin, KL Wagstaff
Machine Learning 95 (1), 1-9, 2014
932014
Clustering with missing values: No imputation required
K Wagstaff
Classification, clustering, and data mining applications, 649-658, 2004
902004
When is constrained clustering beneficial, and why?
KL Wagstaff, S Basu, I Davidson
Ionosphere 58 (60.1), 62-63, 2006
812006
Active constrained clustering by examining spectral eigenvectors
Q Xu, M desJardins, KL Wagstaff
International Conference on Discovery Science, 294-307, 2005
782005
V-fastr: The vlba fast radio transients experiment
RB Wayth, WF Brisken, AT Deller, WA Majid, DR Thompson, SJ Tingay, ...
The Astrophysical Journal 735 (2), 97, 2011
672011
Onboard autonomy on the intelligent payload experiment cubesat mission
S Chien, J Doubleday, DR Thompson, KL Wagstaff, J Bellardo, C Francis, ...
Journal of Aerospace Information Systems 14 (6), 307-315, 2017
562017
Value, cost, and sharing: Open issues in constrained clustering
KL Wagstaff
International workshop on knowledge discovery in inductive databases, 1-10, 2006
562006
Constrained spectral clustering under a local proximity structure assumption
Q Xu, M Desjardins, K Wagstaff
In FLAIRS Conference, 2005
562005
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