[DOCS] Adds more details to the frequent items agg documentation (#86661)

Co-authored-by: Mark Tozzi <mark.tozzi@gmail.com>

docs/reference/aggregations/bucket/frequent-items-aggregation.asciidoc

@@ -49,14 +49,26 @@ A `frequent_items` aggregation looks like this in isolation:
 |`size` | (integer) The number of top item sets to return. | Optional | `10`
 |===
 
+
+[discrete]
+[[frequent-items-fields]]
+==== Fields
+
+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.
+
 [discrete]
 [[frequent-items-minimum-set-size]]
 ==== Minimum set size
 
 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. The higher the minimum set 
-size the less items are returned. For example, the item set `orange, banana, 
-apple` is only returned if the minimum set size is 3 or lower.
+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.
 
 [discrete]
 [[frequent-items-minimum-support]]
@@ -91,6 +103,10 @@ aggregation.
 
 In the following examples, we use the e-commerce {kib} sample data set.
 
+
+[discrete]
+==== Aggregation with two analized fields
+
 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 
@@ -182,6 +198,10 @@ 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.
 
+
+[discrete]
+==== Analizing numeric values by using a runtime field
+
 The frequent items aggregation enables you to bucket numeric values by using 
 <<runtime,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