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1.
Model Based Clustering Algorithms with Applications
by Wei, Wutao, Ph.D.  Purdue University. 2018: 124 pages; 10830711.
2.
Practical Inference-Time Attacks against Machine-Learning Systems and a Defense against Them
by Sharif, Mahmood, Ph.D.  Carnegie Mellon University. 2019: 137 pages; 27662625.
3.
Probabilistic Clustering Ensemble Evaluation for Intrusion Detection
by McElwee, Steven M., Ph.D.  Nova Southeastern University. 2018: 166 pages; 10844875.
4.
Weighted clustering ensembles
by Al-Razgan, Muna Saleh, Ph.D.  George Mason University. 2008: 146 pages; 3323309.
5.
Clustering transient data streams by example and by variable
by Chaovalit, Pimwadee, Ph.D.  University of Maryland, Baltimore County. 2009: 224 pages; 3359104.
7.
Modeling alternatives to solve the connected graph k-clustering problem
by Es-Sakli, Nada, M.S.  Lehigh University. 2011: 74 pages; 1488515.
9.
Attention-Dependent and Continuous Representation Learning using Deep Autoencoders
by Dellana, Ryan, Ph.D.  North Carolina Agricultural and Technical State University. 2019: 104 pages; 22583727.
10.
Studies of spatial clustering of inertial particles in turbulence
by Saw, Ewe Wei, Ph.D.  Michigan Technological University. 2008: 164 pages; 3339459.
11.
Assessing the Robustness of Clustering Methods for use in Vegetation Classification
by Dell, Noah David, M.S.  Southern Illinois University at Edwardsville. 2017: 108 pages; 10275943.
13.
Clustering and entity resolution for semi-structured data
by Zhao, Chuan, Ph.D.  Washington State University. 2011: 208 pages; 3495962.
15.
Stochastic Data Clustering
by Wessell, Charles David, Ph.D.  North Carolina State University. 2011: 102 pages; 3479609.
16.
Applications of k-means and spectral clustering to hyperspectral video sequences
by Sunu, Justin, M.S.  California State University, Long Beach. 2014: 72 pages; 1526959.
17.
Botnet Detection Using Graph Based Feature Clustering
by Akula, Ravi Kiran, M.S.  Mississippi State University. 2018: 69 pages; 10751733.
18.
Solution of Constrained Clustering Problems Through Homotopy Tracking
by Easterling, David R., Ph.D.  Virginia Polytechnic Institute and State University. 2014: 101 pages; 10595248.
19.
Clustering of Large-Scale Protein Datasets
by Abnousi, Armen, Ph.D.  Washington State University. 2018: 118 pages; 10641432.
20.
Interpretable Deep Learning for Natural Language Processing
by Murdoch, William James, Ph.D.  University of California, Berkeley. 2019: 114 pages; 13880811.
21.
Clustering datasets with singular value decomposition
by Douglas, Emmeline P., M.S.  College of Charleston. 2008: 85 pages; 1461189.
22.
Clustering, searching and evaluating biological sequence data
by Ghodsi, Mohammadreza, Ph.D.  University of Maryland, College Park. 2012: 104 pages; 3543455.
23.
Evaluation and Design of Robust Neural Network Defenses
by Carlini, Nicholas, Ph.D.  University of California, Berkeley. 2018: 138 pages; 10931076.
24.
Clustering digital ink content to assist with the grading of student work
by Hatfield, Jared, M.Eng.  University of Louisville. 2011: 65 pages; 1504211.
25.
Exploring the Effects of Frontalization and Data Synthesis on Face Recognition
by Banerjee, Sandipan, Ph.D.  University of Notre Dame. 2019: 189 pages; 27750532.
26.
Computational Models for Auditory Scene Analysis
by Mobin, Shariq A., Ph.D.  University of California, Berkeley. 2019: 67 pages; 13885042.
27.
Two Statistical Methods for Clustering Medicare Claims into Episodes of Care
by Gibbons, Robert Lyon, Ph.D.  University of California, Berkeley. 2011: 135 pages; 3473890.
29.
A comparison of particle swarm optimization algorithms in data clustering
by Chung, Yu-Kuang, M.S.  California State University, Long Beach. 2010: 69 pages; 1486378.
30.
High-speed tracking of rupture and clustering in freely falling granular streams
by Royer, John R., Ph.D.  The University of Chicago. 2009: 110 pages; 3369460.
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