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BottomPercent (DMX)

Returns, in order of increasing rank, the bottom-most rows of a table whose cumulative total is at least a specified percentage.

Syntax

BottomPercent(<table expression>, <rank expression>, <percent>)

Arguments

  • <Table expression>
    The name of a nested table column or table-valued expression.

  • <rank expression>
    A column in the nested table, or expression that evaluates to a column.

  • <percent>
    A double that indicates the total target percentage.

Result Type

A table.

Remarks

The BottomPercent function returns the bottom-most rows in increasing order of rank. The rank is based on the evaluated value of the <rank expression> argument for each row, such that the sum of the <rank expression> values is at least the given percentage that is specified by the <percent> argument. BottomPercent returns the smallest number of elements possible while still meeting the specified percent value.

Examples

The following example creates a prediction query against the Association model that you built in the Basic Data Mining Tutorial.

To understand how BottomPercent works, it may be helpful to first execute a prediction query that returns only the nested table.

SELECT Predict ([Association].[v Assoc Seq Line Items], INCLUDE_STATISTICS, 10)
FROM 
     [Association]
NATURAL PREDICTION JOIN
SELECT (SELECT 'Women''s Mountain Shorts' as [Model]) AS [v Assoc Seq Line Items]) AS t

Note

In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see How to: Create a Singleton Query in the Data Mining Designer.

Example results:

Model

$SUPPORT

$PROBABILITY

$ADJUSTEDPROBABILITY

Sport-100

4334

0.291283016

0.252695851

Water Bottle

2866

0.192620472

0.175205052

Patch kit

2113

0.142012232

0.132389356

Mountain Tire Tube

1992

0.133879965

0.125304948

Mountain-200

1755

0.117951475

0.111260823

Road Tire Tube

1588

0.106727603

0.101229538

Cycling Cap

1473

0.098998589

0.094256014

Fender Set - Mountain

1415

0.095100477

0.090718432

Mountain Bottle Cage

1367

0.091874454

0.087780332

Road Bottle Cage

1195

0.080314537

0.077173962

The BottomPercent function takes the results of this query and returns the smallest-valued rows that sum to the specified percentage.

SELECT 
BottomPercent
    (
    Predict ([Association].[v Assoc Seq Line Items],INCLUDE_STATISTICS,10),
    $SUPPORT,
    50)
FROM 
     [Association]
NATURAL PREDICTION JOIN
(SELECT (SELECT 'Women''s Mountain Shorts' as [Model]) AS [v Assoc Seq Line Items]) AS t

The first argument to the BottomPercent function is the name of a table column. In this example, the nested table is returned by calling the Predict function and using the INCLUDE_STATISTICS argument.

The second argument to the BottomPercent function is the column in the nested table that you use to order the results. In this example, the INCLUDE_STATISTICS option returns the columns $SUPPORT, $PROBABILTY, and $ADJUSTED PROBABILITY. This example uses $SUPPORT because support values are not fractional and therefore are easier to verify.

The third argument to the BottomPercent function specifies the percentage, as a double. To get the rows that represent the bottom 50 percent of the support, you type 50.

Example results:

Model

$SUPPORT

$PROBABILITY

$ADJUSTEDPROBABILITY

Road Bottle Cage

1195

0.080314537

0.077173962

Mountain Bottle Cage

1367

0.091874454

0.087780332

Fender Set - Mountain

1415

0.095100477

0.090718432

Cycling Cap

1473

0.098998589

0.094256014

Road Tire Tube

1588

0.106727603

0.101229538

Mountain-200

1755

0.117951475

0.111260823

Mountain Tire Tube

1992

0.133879965

0.125304948

Note   This example is provided only to illustrate the usage of BottomPercent. Depending on the size of your data set, this query might take a long time to run.