Frequent item sets aggregation
editFrequent item sets aggregation
editA bucket aggregation which finds frequent item sets. It is a form of association rules mining that identifies items that often occur together. Items that are frequently purchased together or log events that tend to co-occur are examples of frequent item sets. Finding frequent item sets helps to discover relationships between different data points (items).
The aggregation reports closed item sets. A frequent item set is called closed
if no superset exists with the same ratio of documents (also known as its
support value). For example, we have the two
following candidates for a frequent item set, which have the same support value:
1. apple, orange, banana
2. apple, orange, banana, tomato
.
Only the second item set (apple, orange, banana, tomato
) is returned, and the
first set – which is a subset of the second one – is skipped. Both item sets
might be returned if their support values are different.
The runtime of the aggregation depends on the data and the provided parameters. It might take a significant time for the aggregation to complete. For this reason, it is recommended to use async search to run your requests asynchronously.
Syntax
editA frequent_item_sets
aggregation looks like this in isolation:
"frequent_item_sets": { "minimum_set_size": 3, "fields": [ {"field": "my_field_1"}, {"field": "my_field_2"} ] }
Table 51. frequent_item_sets
Parameters
Parameter Name |
Description |
Required |
Default Value |
|
(array) Fields to analyze. |
Required |
|
|
(integer) The minimum size of one item set. |
Optional |
|
|
(integer) The minimum support of one item set. |
Optional |
|
|
(integer) The number of top item sets to return. |
Optional |
|
|
(object) Query that filters documents from the analysis |
Optional |
|
Fields
editSupported field types for the analyzed fields are keyword, numeric, ip, date, and arrays of these types. You can also add runtime fields to your analyzed fields.
If the combined cardinality of the analyzed fields are high, the aggregation might require a significant amount of system resources.
You can filter the values for each field by using the include
and exclude
parameters. The parameters can be regular expression strings or arrays of
strings of exact terms. The filtered values are removed from the analysis and
therefore reduce the runtime. If both include
and exclude
are defined,
exclude
takes precedence; it means include
is evaluated first and then
exclude
.
Minimum set size
editThe minimum set size is the minimum number of items the set needs to contain. A
value of 1 returns the frequency of single items. Only item sets that contain at
least the number of minimum_set_size
items are returned. For example, the item
set orange, banana, apple
is returned only if the minimum set size is 3 or
lower.
Minimum support
editThe minimum support value is the ratio of documents that an item set must exist in to be considered "frequent". In particular, it is a normalized value between 0 and 1. It is calculated by dividing the number of documents containing the item set by the total number of documents.
For example, if a given item set is contained by five documents and the total
number of documents is 20, then the support of the item set is 5/20 = 0.25.
Therefore, this set is returned only if the minimum support is 0.25 or lower.
As a higher minimum support prunes more items, the calculation is less resource
intensive. The minimum_support
parameter has an effect on the required memory
and the runtime of the aggregation.
Size
editThis parameter defines the maximum number of item sets to return. The result contains top-k item sets; the item sets with the highest support values. This parameter has a significant effect on the required memory and the runtime of the aggregation.
Filter
editA query to filter documents to use as part of the analysis. Documents that don’t match the filter are ignored when generating the item sets, however still count when calculating the support of an item set.
Use the filter if you want to narrow the item set analysis to fields of interest. Use a top-level query to filter the data set.
Examples
editIn the following examples, we use the e-commerce Kibana sample data set.
Aggregation with two analyzed fields and an exclude
parameter
editIn the first example, the goal is to find out based on transaction data (1.)
from what product categories the customers purchase products frequently together
and (2.) from which cities they make those purchases. We want to exclude results
where location information is not available (where the city name is other
).
Finally, we are interested in sets with three or more items, and want to see the
first three frequent item sets with the highest support.
Note that we use the async search endpoint in this first example.
resp = client.async_search.submit( index="kibana_sample_data_ecommerce", size=0, aggs={ "my_agg": { "frequent_item_sets": { "minimum_set_size": 3, "fields": [ { "field": "category.keyword" }, { "field": "geoip.city_name", "exclude": "other" } ], "size": 3 } } }, ) print(resp)
const response = await client.asyncSearch.submit({ index: "kibana_sample_data_ecommerce", size: 0, aggs: { my_agg: { frequent_item_sets: { minimum_set_size: 3, fields: [ { field: "category.keyword", }, { field: "geoip.city_name", exclude: "other", }, ], size: 3, }, }, }, }); console.log(response);
POST /kibana_sample_data_ecommerce/_async_search { "size":0, "aggs":{ "my_agg":{ "frequent_item_sets":{ "minimum_set_size":3, "fields":[ { "field":"category.keyword" }, { "field":"geoip.city_name", "exclude":"other" } ], "size":3 } } } }
The response of the API call above contains an identifier (id
) of the async
search request. You can use the identifier to retrieve the search results:
resp = client.async_search.get( id="<id>", ) print(resp)
const response = await client.asyncSearch.get({ id: "<id>", }); console.log(response);
GET /_async_search/<id>
The API returns a response similar to the following one:
(...) "aggregations" : { "my_agg" : { "buckets" : [ { "key" : { "category.keyword" : [ "Women's Clothing", "Women's Shoes" ], "geoip.city_name" : [ "New York" ] }, "doc_count" : 217, "support" : 0.04641711229946524 }, { "key" : { "category.keyword" : [ "Women's Clothing", "Women's Accessories" ], "geoip.city_name" : [ "New York" ] }, "doc_count" : 135, "support" : 0.028877005347593583 }, { "key" : { "category.keyword" : [ "Men's Clothing", "Men's Shoes" ], "geoip.city_name" : [ "Cairo" ] }, "doc_count" : 123, "support" : 0.026310160427807486 } ], (...) } }
The array of returned item sets. |
|
The |
|
The number of documents that contain the item set. |
|
The support value of the item set. It is calculated by dividing the number of documents containing the item set by the total number of documents. |
The response shows that the categories customers purchase from most frequently
together are Women's Clothing
and Women's Shoes
and customers from New York
tend to buy items from these categories frequently together. In other words,
customers who buy products labelled Women's Clothing
more likely buy products
also from the Women's Shoes
category and customers from New York most likely
buy products from these categories together. The item set with the second
highest support is Women's Clothing
and Women's Accessories
with customers
mostly from New York. Finally, the item set with the third highest support is
Men's Clothing
and Men's Shoes
with customers mostly from Cairo.
Aggregation with two analyzed fields and a filter
editWe take the first example, but want to narrow the item sets to places in Europe.
For that, we add a filter, and this time, we don’t use the exclude
parameter:
resp = client.async_search.submit( index="kibana_sample_data_ecommerce", size=0, aggs={ "my_agg": { "frequent_item_sets": { "minimum_set_size": 3, "fields": [ { "field": "category.keyword" }, { "field": "geoip.city_name" } ], "size": 3, "filter": { "term": { "geoip.continent_name": "Europe" } } } } }, ) print(resp)
const response = await client.asyncSearch.submit({ index: "kibana_sample_data_ecommerce", size: 0, aggs: { my_agg: { frequent_item_sets: { minimum_set_size: 3, fields: [ { field: "category.keyword", }, { field: "geoip.city_name", }, ], size: 3, filter: { term: { "geoip.continent_name": "Europe", }, }, }, }, }, }); console.log(response);
POST /kibana_sample_data_ecommerce/_async_search { "size": 0, "aggs": { "my_agg": { "frequent_item_sets": { "minimum_set_size": 3, "fields": [ { "field": "category.keyword" }, { "field": "geoip.city_name" } ], "size": 3, "filter": { "term": { "geoip.continent_name": "Europe" } } } } } }
The result will only show item sets that created from documents matching the
filter, namely purchases in Europe. Using filter
, the calculated support
still takes all purchases into acount. That’s different than specifying a query
at the top-level, in which case support
gets calculated only from purchases in
Europe.
Analyzing numeric values by using a runtime field
editThe frequent items aggregation enables you to bucket numeric values by using
runtime fields. The next example demonstrates how to use a script to
add a runtime field to your documents called price_range
, which is
calculated from the taxful total price of the individual transactions. The
runtime field then can be used in the frequent items aggregation as a field to
analyze.
resp = client.search( index="kibana_sample_data_ecommerce", runtime_mappings={ "price_range": { "type": "keyword", "script": { "source": "\n def bucket_start = (long) Math.floor(doc['taxful_total_price'].value / 50) * 50;\n def bucket_end = bucket_start + 50;\n emit(bucket_start.toString() + \"-\" + bucket_end.toString());\n " } } }, size=0, aggs={ "my_agg": { "frequent_item_sets": { "minimum_set_size": 4, "fields": [ { "field": "category.keyword" }, { "field": "price_range" }, { "field": "geoip.city_name" } ], "size": 3 } } }, ) print(resp)
const response = await client.search({ index: "kibana_sample_data_ecommerce", runtime_mappings: { price_range: { type: "keyword", script: { source: "\n def bucket_start = (long) Math.floor(doc['taxful_total_price'].value / 50) * 50;\n def bucket_end = bucket_start + 50;\n emit(bucket_start.toString() + \"-\" + bucket_end.toString());\n ", }, }, }, size: 0, aggs: { my_agg: { frequent_item_sets: { minimum_set_size: 4, fields: [ { field: "category.keyword", }, { field: "price_range", }, { field: "geoip.city_name", }, ], size: 3, }, }, }, }); console.log(response);
GET kibana_sample_data_ecommerce/_search { "runtime_mappings": { "price_range": { "type": "keyword", "script": { "source": """ def bucket_start = (long) Math.floor(doc['taxful_total_price'].value / 50) * 50; def bucket_end = bucket_start + 50; emit(bucket_start.toString() + "-" + bucket_end.toString()); """ } } }, "size": 0, "aggs": { "my_agg": { "frequent_item_sets": { "minimum_set_size": 4, "fields": [ { "field": "category.keyword" }, { "field": "price_range" }, { "field": "geoip.city_name" } ], "size": 3 } } } }
The API returns a response similar to the following one:
(...) "aggregations" : { "my_agg" : { "buckets" : [ { "key" : { "category.keyword" : [ "Women's Clothing", "Women's Shoes" ], "price_range" : [ "50-100" ], "geoip.city_name" : [ "New York" ] }, "doc_count" : 100, "support" : 0.0213903743315508 }, { "key" : { "category.keyword" : [ "Women's Clothing", "Women's Shoes" ], "price_range" : [ "50-100" ], "geoip.city_name" : [ "Dubai" ] }, "doc_count" : 59, "support" : 0.012620320855614974 }, { "key" : { "category.keyword" : [ "Men's Clothing", "Men's Shoes" ], "price_range" : [ "50-100" ], "geoip.city_name" : [ "Marrakesh" ] }, "doc_count" : 53, "support" : 0.011336898395721925 } ], (...) } }
The response shows the categories that customers purchase from most frequently together, the location of the customers who tend to buy items from these categories, and the most frequent price ranges of these purchases.