Frequent items aggregationedit

This functionality is in technical preview and may be changed or removed in a future release. Elastic will apply best effort to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

A 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.

Syntaxedit

A frequent_items aggregation looks like this in isolation:

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

Table 46. frequent_items Parameters

Parameter Name

Description

Required

Default Value

fields

(array) Fields to analyze.

Required

minimum_set_size

(integer) The minimum size of one item set.

Optional

1

minimum_support

(integer) The minimum support of one item set.

Optional

0.1

size

(integer) The number of top item sets to return.

Optional

10

Fieldsedit

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.

Minimum set sizeedit

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 only returned if the minimum set size is 3 or lower.

Minimum supportedit

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.

Sizeedit

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.

Examplesedit

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

Aggregation with two analized fieldsedit

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 array of returned item sets.

The key object contains one item set. In this case, it consists of two values of the category.keyword field and one value of the geoip.city_name.

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 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.

Analizing numeric values by using a runtime fieldedit

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 that 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.