650 results on '"Steinley, Douglas"'
Search Results
152. The p-Median Model as a Tool for Clustering Psychological Data
153. Cross validation issues in multiobjective clustering
154. An implicit enumeration method for an exact test of weighted kappa
155. Stability analysis in K-means clustering
156. Supplementary_material – Supplemental material for Item Responses in Quantity–Frequency Questionnaires: Implications for Data Generalizability
157. Clustering, Seriation, and Subset Extraction of Confusion Data
158. Profiling Local Optima in K-Means Clustering: Developing a Diagnostic Technique
159. K-means clustering: A half-century synthesis
160. Properties of the Hubert-Arabie Adjusted Rand Index
161. Local Optima in K-Means Clustering: What You Donʼt Know May Hurt You
162. Combining diversity and dispersion criteria for anticlustering: A bicriterion approach
163. Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models
164. The influence of sample selection on the structure of psychopathology symptom networks: An example with alcohol use disorder.
165. Item Responses in Quantity–Frequency Questionnaires: Implications for Data Generalizability
166. Deriving alternative criteria sets for alcohol use disorders using statistical optimization: Results from the National Survey on Drug Use and Health.
167. Estimating transdiagnostic symptom networks: The problem of “skip outs” in diagnostic interviews.
168. K-medoids inverse regression
169. Deterministic blockmodelling of signed and two‐mode networks: A tutorial with software and psychological examples.
170. Combinatorial Optimization of Clustering Decisions: An Approach to Refine Psychiatric Diagnoses.
171. Combining diversity and dispersion criteria for anticlustering: A bicriterion approach.
172. Item Responses in Quantity–Frequency Questionnaires: Implications for Data Generalizability.
173. K-medoids inverse regression.
174. Affinity propagation: An exemplar-based tool for clustering in psychological research
175. Residual analysis for unidimensional scaling in the L2-norm
176. Integrating linear discriminant analysis, polynomial basis expansion, and genetic search for two-group classification
177. Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL-Median Heuristic
178. Criteria Definitions and Network Relations: The Importance of Criterion Thresholds
179. Detecting Clusters/Communities in Social Networks
180. Measuring and testing the agreement of matrices
181. A note on the expected value of the Rand index
182. Desistance and Severity of Alcohol Use Disorder: A Lifespan-Developmental Investigation
183. Who’s Got the Booze? The Role of Access to Alcohol in the Relations Between Social Status and Individual Use
184. A simulated annealing heuristic for maximum correlation core/periphery partitioning of binary networks
185. An Experimental Investigation of Drunk Personality Using Self and Observer Reports
186. Development of the Medical Maximizer-Minimizer Scale.
187. Residual analysis for unidimensional scaling in the L2-norm.
188. Integrating linear discriminant analysis, polynomial basis expansion, and genetic search for two-group classification.
189. Affinity propagation: An exemplar‐based tool for clustering in psychological research.
190. Social network drinking and family history contribute equally to first-onset alcohol dependence in high risk adults
191. Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors
192. Local Optima in Mixture Modeling
193. Modeling Between-Subject Variability in Decision Strategies via Statistical Clustering: A p -Median Approach
194. Examining the effect of initialization strategies on the performance of Gaussian mixture modeling
195. Using Cohen's κ for Community Detection in Social Networks
196. Affinity Propagation and Uncapacitated Facility Location Problems
197. Data Clustering: Theory, Algorithms, and Applications Guojun Gan
198. A note on the expected value of the Rand index.
199. Editorial.
200. Modeling Between‐Subject Variability in Decision Strategies via Statistical Clustering: A <italic>p</italic>‐Median Approach.
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