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What is a Concept Hierarchies?
A concept hierarchy represents a series of mappings from a set of low-level concepts to larger-level, more general concepts. Concept hierarchy organizes information or concepts in a hierarchical structure or a specific partial order, which are used for defining knowledge in brief, high-level methods, and creating possible mining knowledge at several levels of abstraction.
A conceptual hierarchy includes a set of nodes organized in a tree, where the nodes define values of an attribute known as concepts. A specific node, “ANY”, is constrained for the root of the tree. A number is created to the level of each node in a conceptual hierarchy. The level of the root node is one. The level of a non-root node is one more the level of its parent level number.
Because values are defined by nodes, the levels of nodes can also be used to describe the levels of values. Concept hierarchy enables raw information to be managed at a higher and more generalized level of abstraction. There are several types of concept hierarchies which are as follows −
Schema Hierarchy − Schema hierarchy represents the total or partial order between attributes in the database. It can define existing semantic relationships between attributes. In a database, more than one schema hierarchy can be generated by using multiple sequences and grouping of attributes.
Set-Grouping Hierarchy − A set-grouping hierarchy constructs values for a given attribute or dimension into groups or constant range values. It is also known as instance hierarchy because the partial series of the hierarchy is represented on the set of instances or values of an attribute. These hierarchies have more functional sense and are so approved than other hierarchies.
Operation-Derived Hierarchy − Operation-derived hierarchy is represented by a set of operations on the data. These operations are defined by users, professionals, or the data mining system. These hierarchies are usually represented for mathematical attributes. Such operations can be as easy as range value comparison, as difficult as a data clustering and data distribution analysis algorithm.
Rule-based Hierarchy − In a rule-based hierarchy either a whole concept hierarchy or an allocation of it is represented by a set of rules and is computed dynamically based on the current information and rule definition. A lattice-like architecture is used for graphically defining this type of hierarchy, in which each child-parent route is connected with a generalization rule.
The static and dynamic generation of concept hierarchy is based on data sets. In this context, the generation of a concept hierarchy depends on a static or dynamic data set is known as the static or dynamic generation of concept hierarchy.
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