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1.
Probabilistic Clustering Ensemble Evaluation for Intrusion Detection
by McElwee, Steven M., Ph.D.  Nova Southeastern University. 2018: 166 pages; 10844875.
3.
Using and Manipulating Probabilistic Connectivity in Social Networks
by DuBois, Thomas, Ph.D.  University of Maryland, College Park. 2011: 194 pages; 3461511.
4.
Clustering transient data streams by example and by variable
by Chaovalit, Pimwadee, Ph.D.  University of Maryland, Baltimore County. 2009: 224 pages; 3359104.
5.
Statistical Relational Learning through Structural Analogy and Probabilistic Generalization
by Halstead, Daniel T., Ph.D.  Northwestern University. 2011: 159 pages; 3488474.
7.
Clustering and entity resolution for semi-structured data
by Zhao, Chuan, Ph.D.  Washington State University. 2011: 208 pages; 3495962.
8.
Probabilistic learning for analysis of sensor-based human activity data
by Hutchins, Jonathan, Ph.D.  University of California, Irvine. 2010: 245 pages; 3432162.
9.
Probabilistic models of phase variables for visual representation and neural dynamics
by Cadieu, Charles, Ph.D.  University of California, Berkeley. 2009: 120 pages; 3402686.
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WLAN Access Points RSSI-Fingerprinting and Compressive Sensing for Indoor Positioning Systems
by Al-Moukhles, Hussein Nasser Wazeer, Ph.D.  Western Michigan University. 2018: 131 pages; 13877013.
13.
Large-Scale, Hybrid Approaches for Recommending Web Pages Based on Content and User's Previous Click Patterns
by Sharif, Mohammad Amir, Ph.D.  University of Louisiana at Lafayette. 2015: 108 pages; 10002452.
14.
Application of Graphical Models in Protein-Protein Interactions and Dynamics
by Vajdi Hoojghan, Amir, Ph.D.  University of Massachusetts Boston. 2018: 92 pages; 10982841.
15.
Studies of spatial clustering of inertial particles in turbulence
by Saw, Ewe Wei, Ph.D.  Michigan Technological University. 2008: 164 pages; 3339459.
16.
Modeling alternatives to solve the connected graph k-clustering problem
by Es-Sakli, Nada, M.S.  Lehigh University. 2011: 74 pages; 1488515.
17.
Weighted clustering ensembles
by Al-Razgan, Muna Saleh, Ph.D.  George Mason University. 2008: 146 pages; 3323309.
18.
Beyond dynamic textures: A family of stochastic dynamical models for video with applications to computer vision
by Chan, Antoni Bert, Ph.D.  University of California, San Diego. 2008: 291 pages; 3331461.
19.
Methods in Computational Cosmology
by Vakili, Mohammadjavad, Ph.D.  New York University. 2017: 235 pages; 10260795.
20.
Generative Probabilistic Models for Analysis of Communication Event Data with Applications to Email Behavior
by Navaroli, Nicholas Martin, Ph.D.  University of California, Irvine. 2014: 249 pages; 3668831.
21.
Stochastic Data Clustering
by Wessell, Charles David, Ph.D.  North Carolina State University. 2011: 102 pages; 3479609.
22.
Clustering algorithms for next-generation sequencing data from heterogenous populations
by Prabhakara, Shruthi, Ph.D.  The Pennsylvania State University. 2012: 126 pages; 3737327.
24.
A location fingerprint framework towards efficient wireless indoor positioning systems
by Swangmuang, Nattapong, Ph.D.  University of Pittsburgh. 2008: 146 pages; 3349244.
25.
Neuronal representations underlying probabilistic sequence discrimination
by Kiggins, Justin, Ph.D.  University of California, San Diego. 2016: 107 pages; 10238991.
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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.
29.
Integrating Exponential Dispersion Models to Latent Structures
by Basbug, Mehmet Emin, Ph.D.  Princeton University. 2017: 125 pages; 10254057.
30.
Model Based Clustering Algorithms with Applications
by Wei, Wutao, Ph.D.  Purdue University. 2018: 124 pages; 10830711.
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