Frequent pattern mining functions
Frequent pattern mining functions analyze multidimensional data to extract attribute combinations with significant differences and quantify their impact. You can configure parameters to optimize mining results.
get_patterns
get_patterns is a frequent itemset mining operator. It mines frequent items, merges and deduplicates the results, and extracts templates (frequent itemsets) from tabular data.
Syntax
get_patterns($TABLE, $HEADER, $PARAM)Parameters
|
Parameter |
Data type |
Required |
Description |
|
$TABLE |
row<array<T>, array<E>, ..., array<F>> |
Yes |
The input table for frequent item mining. Each column is a dimension column to be mined. |
|
$HEADER |
array<varchar> |
Yes |
The column names, corresponding to the columns in $TABLE. The number of header names must match the number of columns. |
|
$PARAM |
varchar |
No |
For more information, see PARAM parameter description. |
PARAM parameter description
|
Parameter |
Description |
Parameter type |
Required |
Default value |
Value range |
|
minimum_support_fraction |
The minimum support threshold for output patterns in the test group. For example, a pattern that appears with a frequency of 0.1 has a support of 0.1. Adjust this value to control the number of patterns returned. |
double |
No |
0.05 |
(0, 1) |
Examples
-
Query analysis:
This feature is in public preview. You must manually add the `set session enable_remote_functions=true` statement. This requirement will be removed in a future version.
(*)| set session enable_remote_functions=true ; with t0 as (select JSON_EXTRACT_SCALAR(entity, '$.platform') AS platform, JSON_EXTRACT_SCALAR(entity, '$.region') AS region, cast(value as double) as value, if((value > 100), 'true', 'false') as anomaly_label from log), t1 as ( select array_agg(platform) as platform, array_agg(region) as region, array_agg(anomaly_label) as anomaly_label, array_agg(value) as value from t0), t2 as (select row(platform, region) as table_row from t1), t3 as (select get_patterns(table_row, ARRAY['platform', 'region']) as ret from t2) select * from t3 -
Outputs:
[["platform=eBay","platform=edX","platform=Amazon","platform=Skillshare","platform=Shopify","platform=Khan Academy","platform=Coursera","platform=Udemy","platform=Alibaba","platform=Taobao","platform=Snapchat","platform=Amazon Prime","platform=YouTube","platform=Hulu","platform=Peloton","platform=Twitter","platform=Fitbit","platform=Nike Training","platform=LinkedIn","platform=Instagram","platform=Disney+","platform=Strava","platform=MyFitnessPal","platform=Facebook","platform=Netflix","platform=Console","platform=Samsung SmartThings","platform=Apple HomeKit","platform=Mobile","platform=PC","platform=Google Home","platform=VR"],[156960,149760,148320,148320,146880,145440,139680,136800,133920,133920,96480,95040,92160,90720,90720,89280,89280,87840,84960,83520,83520,82080,82080,77760,70560,46080,41760,41760,34560,33120,31680,30240],null,null]
Return value description
|
Parameter |
Type |
Description |
Example |
|
$RET.patterns |
array<varchar> |
Frequent itemsets extracted as table templates. Each varchar is an expression with conditions connected by AND, such as |
|
|
$RET.test_supports |
array<bigint> |
The occurrence count of each template. |
|
|
$RET.labels |
array<bigint> |
Reserved for future automatic data classification. Currently always returns |
|
|
$RET.error_msg |
array<varchar>/null |
The error message. Returns |
|