| { |
| "field_id": "F034", |
| "field": "cluster analysis", |
| "directions": [ |
| { |
| "direction_id": "G0", |
| "name": "Representation and relational structure for cluster recovery", |
| "scope": "Learn, construct, or compress features and relationships between observations so that latent clusters can be distinguished and recovered; fully encompasses joint representation-and-assignment learning, self-expressive subspace relationships, and cluster recovery after dimensionality reduction." |
| }, |
| { |
| "direction_id": "G1", |
| "name": "Integration of complementary clustering evidence", |
| "scope": "Reconcile complementary evidence from different views or existing clustering results into a shared cluster structure; fully encompasses jointly available views, recovery when views are missing, and consensus aggregation of base partitions." |
| }, |
| { |
| "direction_id": "G2", |
| "name": "Clustering from graph relationships", |
| "scope": "Use graph connections, attributes, or relational objectives to determine node partitions; fully encompasses spectral and graph operators, learned node clustering, and discrete graph-partition optimization." |
| }, |
| { |
| "direction_id": "G3", |
| "name": "Neighborhood-defined cluster discovery", |
| "scope": "Form and separate clusters according to local concentration, connectivity, and neighborhood geometry, including irregularly shaped groups." |
| }, |
| { |
| "direction_id": "G4", |
| "name": "Representative-based partition fitting", |
| "scope": "Form clusters by fitting probabilistic components or selecting centers, prototypes, and medoids, and study fitting quality and computation." |
| }, |
| { |
| "direction_id": "G5", |
| "name": "Clustering under outlier contamination", |
| "scope": "Recover useful partitions or components in the presence of outlying or adversarial observations, without allowing anomalous observations to dominate estimation and assignments." |
| }, |
| { |
| "direction_id": "G6", |
| "name": "Fairness-constrained clustering", |
| "scope": "Make cluster memberships, centers, costs, or hierarchies satisfy explicit group-fairness requirements." |
| }, |
| { |
| "direction_id": "G7", |
| "name": "Clustering guided by supplied side information", |
| "scope": "Use limited supplied information about memberships or relationships between observations to guide cluster assignments and recovery." |
| }, |
| { |
| "direction_id": "G8", |
| "name": "Temporal-pattern clustering", |
| "scope": "Define groups according to the alignment, shape, dynamics, or predictive behavior of time series or subsequences." |
| }, |
| { |
| "direction_id": "G9", |
| "name": "Structured and intelligible cluster descriptions", |
| "scope": "Make clustering results more than uncharacterized flat assignments by presenting nested relationships or intelligible group definitions and explanations." |
| }, |
| { |
| "direction_id": "G10", |
| "name": "Assessment and validation of cluster structure", |
| "scope": "Determine whether data support clusters and how many, or measure and compare completed clusterings and assess the trustworthiness of their evaluation basis." |
| }, |
| { |
| "direction_id": "G11", |
| "name": "Clustering with restricted data access and disclosure", |
| "scope": "Obtain useful clustering when raw observations cannot be freely pooled or released." |
| } |
| ] |
| } |
|
|
