Asked by: Hester Suarez
asked in category: General Last Updated: 4th June, 2020

What is data Discretization and concept hierarchy generation?

Data Discretization & Concept hierarchy generation. Data discretization techniques can be used to reduce the number of values for a given continuous attribute by dividing the range of the attribute into intervals. A concept hierarchy for a given numerical attribute defines a discretization of the attribute.

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Also, what is meant by hierarchy generation?

Concept Hierarchy reduce the data by collecting and replacing low level concepts (such as numeric values for the attribute age) by higher level concepts (such as young, middle-aged, or senior). Concept hierarchy generation for numeric data is as follows: Binning (see sections before)

Similarly, what is data discretization in data mining? Discretization is the process of putting values into buckets so that there are a limited number of possible states. The buckets themselves are treated as ordered and discrete values. You can discretize both numeric and string columns. There are several methods that you can use to discretize data.

Hereof, what is data discretization?

Data discretization is defined as a process of converting continuous data attribute values into a finite set of intervals with minimal loss of information. Our results show that our method delivers on the average 31% less classification errors than many previously known discretization methods.

What is data warehouse hierarchy?

Hierarchies are logical structures that use ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse.

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