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The General Concept Lattice (GCL) proposes a novel general construction of concept hierarchy from formal context, where the conventional Formal Concept Lattice based on Formal Concept Analysis (FCA) only serves as a substructure. [1] [2] [3] The formal context is a data table of heterogeneous relations illustrating how objects carrying attributes.
A nested hierarchy or inclusion hierarchy is a hierarchical ordering of nested sets. [3] The concept of nesting is exemplified in Russian matryoshka dolls. Each doll is encompassed by another doll, all the way to the outer doll. The outer doll holds all of the inner dolls, the next outer doll holds all the remaining inner dolls, and so on.
In information science, formal concept analysis (FCA) is a principled way of deriving a concept hierarchy or formal ontology from a collection of objects and their properties. Each concept in the hierarchy represents the objects sharing some set of properties; and each sub-concept in the hierarchy represents a subset of the objects (as well as ...
The COBWEB data structure is a hierarchy (tree) wherein each node represents a given concept. Each concept represents a set (actually, a multiset or bag) of objects, each object being represented as a binary-valued property list. The data associated with each tree node (i.e., concept) are the integer property counts for the objects in that concept.
Nominal data is often compared to ordinal and ratio data to determine if individual data points influence the behavior of quantitatively driven datasets. [1] [4] For example, the effect of race (nominal) on income (ratio) could be investigated by regressing the level of income upon one or more dummy variables that specify race. When nominal ...
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. [1] Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio.
A hierarchical database model is a data model in which the data is organized into a tree-like structure. The data are stored as records which is a collection of one or more fields. Each field contains a single value, and the collection of fields in a record defines its type.
The MECE principle has been used in the business mapping process wherein the optimum arrangement of information is exhaustive and does not double count at any level of the hierarchy. Examples of MECE arrangements include categorizing people by year of birth (assuming all years are known), apartments by their building number, letters by postmark ...