[DOCS] Edits frequent items aggregation (#91564)

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

@@ -24,7 +24,7 @@ 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, async search>> to run your 
+reason, it is recommended to use <<async-search,async search>> to run your 
 requests asynchronously.
 
 
@@ -73,7 +73,7 @@ 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 only returned if the minimum set size is 3 or 
+set `orange, banana, apple` is returned only if the minimum set size is 3 or 
 lower.
 
 [discrete]
@@ -123,7 +123,7 @@ In the following examples, we use the e-commerce {kib} sample data set.
 
 
 [discrete]
-==== Aggregation with two analized fields
+==== Aggregation with two analyzed 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 
@@ -131,7 +131,7 @@ 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, async search>> endpoint in this first 
+Note that we use the <<async-search,async search>> endpoint in this first 
 example.
 
 [source,console]
@@ -228,8 +228,8 @@ 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 
+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 
@@ -269,8 +269,8 @@ POST /kibana_sample_data_ecommerce/_async_search
 // TEST[skip:setup kibana sample data]
 
 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 to specifying a query at 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.
 
 
@@ -279,7 +279,7 @@ 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,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 
+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.