Frequent items aggregation

A frequent_items aggregation looks like this in isolation:

"frequent_items": {
  "minimum_set_size": 3,
  "fields": [
    {"field": "my_field_1"},
    {"field": "my_field_2"}
  ]
}

Supported 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, then the aggregation might require a significant amount of system resources.

The 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.

The 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.

This 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.

A 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.

In the following examples, we use the e-commerce Kibana sample data set.

In 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 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.

POST /kibana_sample_data_ecommerce/_async_search
{
  "size": 0,
  "aggs": {
    "my_agg": {
      "frequent_items": {
        "minimum_set_size": 3,
        "fields": [
          { "field": "category.keyword" },
          { "field": "geoip.city_name" }
        ],
        "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:

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 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 togeher. 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.

We take the first example, but want to narrow the item sets to places in Europe. For that we add a filter:

POST /kibana_sample_data_ecommerce/_async_search
{
  "size": 0,
  "aggs": {
    "my_agg": {
      "frequent_items": {
        "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.

The 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.

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_items": {
        "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.