NXsimilarity_grouping¶
Status:
base class, extends NXobject
Description:
Metadata to the results of a similarity grouping analysis.
Similarity grouping analyses can be supervised segmentation or machine learning clustering algorithms. These are routine methods which partition the member of a set of objects/geometric primitives into (sub-)groups, features of different type. A plethora of algorithms have been proposed which can be applied also on geometric primitives like points, triangles, or (abstract) features aka objects (including categorical sub-groups).
This base class considers metadata and results of one similarity grouping analysis applied to a set in which objects are either categorized as noise or belonging to a cluster. As the results of the analysis each similarity group, here called feature aka object can get a number of numerical and/or categorical labels.
Symbols:
The symbols used in the schema to specify e.g. dimensions of arrays.
c: Cardinality of the set.
n_lbl_num: Number of numerical labels per object.
n_lbl_cat: Number of categorical labels per object.
n_features: Total number of similarity groups aka features, objects, clusters.
- Groups cited:
Structure:
cardinality: (optional) NX_UINT {units=NX_UNITLESS}
Number of members in the set which is partitioned into features.
number_of_numeric_labels: (optional) NX_UINT {units=NX_UNITLESS}
How many numerical labels does each feature have.
number_of_categorical_labels: (optional) NX_UINT {units=NX_UNITLESS}
How many categorical labels does each feature have.
identifier_offset: (optional) NX_UINT (Rank: 1, Dimensions: [n_lbl_num]) {units=NX_UNITLESS}
Which identifier is the first to be used to label a cluster.
The value should be chosen in such a way that special values can be resolved: * identifier_offset-1 indicates an object belongs to no cluster. * identifier_offset-2 indicates an object belongs to the noise category. Setting for instance identifier_offset to 1 recovers the commonly used case that objects of the noise category get values to -1 and unassigned points to 0. Numerical identifier have to be strictly increasing.
numerical_label: (optional) NX_UINT (Rank: 2, Dimensions: [c, n_lbl_num]) {units=NX_UNITLESS}
Matrix of numerical label for each member in the set. For classical clustering algorithms this can for instance encode the cluster_identifier.
categorical_label: (optional) NX_CHAR (Rank: 2, Dimensions: [c, n_lbl_cat])
Matrix of categorical attribute data for each member in the set.
statistics: (optional) NXprocess
In addition to the detailed storage which members was grouped to which feature/group summary statistics are stored under this group.
number_of_unassigned_members: (optional) NX_UINT (Rank: 1, Dimensions: [n_lbl_num]) {units=NX_UNITLESS}
Total number of members in the set which are categorized as unassigned.
noise: (optional) NX_UINT (Rank: 1, Dimensions: [n_lbl_num]) {units=NX_UNITLESS}
Total number of members in the set which are categorized as noise.
number_of_features: (optional) NX_UINT {units=NX_UNITLESS}
Total number of clusters (excluding noise and unassigned).
feature_identifier: (optional) NX_UINT (Rank: 2, Dimensions: [n_features, n_lbl_num]) {units=NX_UNITLESS}
Array of numerical identifier of each feature (cluster).
feature_member_count: (optional) NX_UINT (Rank: 2, Dimensions: [n_features, n_lbl_num]) {units=NX_UNITLESS}
Array of number of members for each feature.
Hypertext Anchors¶
List of hypertext anchors for all groups, fields, attributes, and links defined in this class.
