typo fixes in association rule docs

rasbt · Mar 14, 2018 · 5ea3577 · 5ea3577
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diff --git a/docs/sources/user_guide/frequent_patterns/apriori.ipynb b/docs/sources/user_guide/frequent_patterns/apriori.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Apriori is a popular algorithm [1] for extracting frequent itemsets with applications in association rule learning. The apriori algorithm has been designed to operate on databases containing transactions, such as purchases by customers of a store. A itemset is considered as \"frequent\" if it meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%), a frequent itemset is defined as a set of items that occur togehter in at least 50% of all transactions in the database."
+    "Apriori is a popular algorithm [1] for extracting frequent itemsets with applications in association rule learning. The apriori algorithm has been designed to operate on databases containing transactions, such as purchases by customers of a store. An itemset is considered as \"frequent\" if it meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%), a frequent itemset is defined as a set of items that occur together in at least 50% of all transactions in the database."
    ]
   },
   {

diff --git a/docs/sources/user_guide/frequent_patterns/association_rules.ipynb b/docs/sources/user_guide/frequent_patterns/association_rules.ipynb
@@ -46,7 +46,10 @@
     "\n",
     "- introduced in [3]\n",
     "\n",
-    "The support metric is defined for itemsets, not assocication rules, and computes the proportion of transactions that contain the antecedant A. Typically, support is used to measure the abundance or frequency (often interpreted as significance or importance) of an itemset in a database. We refer to an itemset as a \"frequent itemset\" if you support is larger than a specified minimum-support threshold. Note that due to the *downward closure* proporty, all subsets of a frequent itemset are also frequent.\n",
+    "The support metric is defined for itemsets, not assocication rules. The table produced by the association rule mining algorithm contains three different support metrics: 'antecedant support', 'consequent support', and 'support'. Here, 'antecedant support' computes the proportion of transactions that contain the antecedant A, and 'consequent support' computes the support for the itemset of the consequent C. The 'support' metric then computes the support of the combined itemset A $\\cup$ C -- note that 'support' depends on 'antecedant support' and 'consequent support' via min('antecedant support', 'consequent support').\n",
+    "\n",
+    "\n",
+    "Typically, support is used to measure the abundance or frequency (often interpreted as significance or importance) of an itemset in a database. We refer to an itemset as a \"frequent itemset\" if you support is larger than a specified minimum-support threshold. Note that in general, due to the *downward closure* property, all subsets of a frequent itemset are also frequent.\n",
     "\n",
     "\n",
     "#### 'confidence':  \n",