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Content Types (Data Mining)

In Microsoft SQL Server 2005 Analysis Services (SSAS), you can define the data types for columns in a mining structure, to affect how algorithms process the data in those columns when you create mining models. However, defining the data types of columns gives algorithms information only about the type of data in the columns—it does not provide information about the behavior of that data. For this reason, each data mining data type in Analysis Services supports one or more content types, which you can use to describe the behavior of the content that the columns contain. For example, if the content in a column repeats in a specific interval, such as days of the week, you can specify the content type of that column as cyclical.

The following list describes the content types in Analysis Services, and identifies the data types that support each type. In addition to the content types listed here, you can use classified columns to define content types for some data types. For more information about classified columns, see Classified Columns. For more information about data types, see Data Types (Data Mining).

DISCRETE

The column contains discrete values. For example, a gender column is a typical discrete attribute column, in that the data represents a finite, counted number of gender categories. The values in a discrete attribute column do not imply ordered data, even if the values are numeric; the values are clearly separated, and there is no possibility of fractional values. Telephone area codes are a good example of discrete data that is numeric.

This content type is supported by all data mining data types in Analysis Services.

CONTINUOUS

The column contains values that represent a continuous set of numeric data. Unlike a discrete column, which represents finite, counted data, a continuous column represents measurement data, and it is possible for the data to contain an infinite number of fractional values. An income column is an example of a continuous attribute column.

This content type is supported by the following data types: Date, Double, and Long.

DISCRETIZED

The column contains values that represent groups, or buckets, of values that are derived from a continuous column. The buckets are treated as ordered and discrete values. For more information about discretizing data, see Discretization Methods.

This content type is supported by the following data types: Date, Double, Long, and Text.

KEY

The column uniquely identifies a row.

This content type is supported by the following data types: Date, Double, Long, and Text.

KEY SEQUENCE

The column is a specific type of key where the values represent a sequence of events. The values are ordered and do not have to be an equal distance apart.

This content type is supported by the following data types: Double, Long, Text, and Date.

KEY TIME

The column is a specific type of key where the values represent values that are ordered and that occur on a time scale

This content type is supported by the following data types: Double, Long, and Date.

ORDERED

The column contains values that define an ordered set. However, the ordered set does not imply any distance or magnitude relationship between values in the set. For example, if an ordered attribute column contains information about skill levels in rank order from one to five, there is no implied information in the distance between skill levels; a skill level of five is not necessarily five times better than a skill level of one.

Ordered attribute columns are considered to be discrete in terms of content type.

This content type is supported by all the data mining data types in Analysis Services.

CYCLICAL

The column contains values that represent a cyclical ordered set. For example, the numbered days of the week is a cyclical ordered set, because day number one follows day number seven.

Cyclical columns are considered both ordered and discrete in terms of content type.

This content type is supported by all the data mining data types in Analysis Services.

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