diff --git a/docs/sources/CHANGELOG.md b/docs/sources/CHANGELOG.md
index b02420d27..26280f545 100755
--- a/docs/sources/CHANGELOG.md
+++ b/docs/sources/CHANGELOG.md
@@ -23,6 +23,8 @@ The CHANGELOG for the current development version is available at
 
 - Fixes an edge-case bug where decision regions plots didn't have unique colors ([#1157](https://github.com/rasbt/mlxtend/issues/1157) via [mariam851](https://github.com/mariam851))
 
+- Reject `min_support` values outside the documented `(0, 1]` interval in `apriori`, `fpgrowth`, `fpmax`, and `hmine`. The previous check only caught `<= 0`, so passing e.g. `min_support=2` silently returned an empty result ([#864](https://github.com/rasbt/mlxtend/issues/864) via [jbbqqf](https://github.com/jbbqqf))
+
 - Add a `top_k` argument to `ExhaustiveFeatureSelector.get_metric_dict()` so callers can request only the highest-scoring subsets before converting the result to a DataFrame ([#610](https://github.com/rasbt/mlxtend/issues/610) via [jbbqqf](https://github.com/jbbqqf))
 
 - `minmax_scaling` no longer returns silent NaNs for constant columns; constant columns are now collapsed to `min_val`, mirroring the existing contract of `standardize`. ([#1167](https://github.com/rasbt/mlxtend/issues/1167) via [jbbqqf](https://github.com/jbbqqf))
diff --git a/mlxtend/frequent_patterns/apriori.py b/mlxtend/frequent_patterns/apriori.py
index 0cfd00f30..6a8c14776 100644
--- a/mlxtend/frequent_patterns/apriori.py
+++ b/mlxtend/frequent_patterns/apriori.py
@@ -258,7 +258,7 @@ def _support(_x, _n_rows, _is_sparse):
         out = np.sum(_x, axis=0) / _n_rows
         return np.array(out).reshape(-1)
 
-    if min_support <= 0.0:
+    if min_support <= 0.0 or min_support > 1.0:
         raise ValueError(
             "`min_support` must be a positive "
             "number within the interval `(0, 1]`. "
diff --git a/mlxtend/frequent_patterns/fpgrowth.py b/mlxtend/frequent_patterns/fpgrowth.py
index 5ebe4e56f..85bf8449d 100644
--- a/mlxtend/frequent_patterns/fpgrowth.py
+++ b/mlxtend/frequent_patterns/fpgrowth.py
@@ -77,7 +77,7 @@ def fpgrowth(
     """
     fpc.valid_input_check(df, null_values)
 
-    if min_support <= 0.0:
+    if min_support <= 0.0 or min_support > 1.0:
         raise ValueError(
             "`min_support` must be a positive "
             "number within the interval `(0, 1]`. "
diff --git a/mlxtend/frequent_patterns/fpmax.py b/mlxtend/frequent_patterns/fpmax.py
index 859aa63e3..2516ec290 100644
--- a/mlxtend/frequent_patterns/fpmax.py
+++ b/mlxtend/frequent_patterns/fpmax.py
@@ -79,7 +79,7 @@ def fpmax(
     """
     fpc.valid_input_check(df, null_values)
 
-    if min_support <= 0.0:
+    if min_support <= 0.0 or min_support > 1.0:
         raise ValueError(
             "`min_support` must be a positive "
             "number within the interval `(0, 1]`. "
diff --git a/mlxtend/frequent_patterns/hmine.py b/mlxtend/frequent_patterns/hmine.py
index 73f2ecf9c..f58f862c0 100644
--- a/mlxtend/frequent_patterns/hmine.py
+++ b/mlxtend/frequent_patterns/hmine.py
@@ -77,7 +77,7 @@ def hmine(
     """
 
     fpc.valid_input_check(df)
-    if min_support <= 0.0:
+    if min_support <= 0.0 or min_support > 1.0:
         raise ValueError(
             "`min_support` must be a positive "
             "number within the interval `(0, 1]`. "
diff --git a/mlxtend/frequent_patterns/tests/test_fpbase.py b/mlxtend/frequent_patterns/tests/test_fpbase.py
index 3d9bedd30..6699063af 100644
--- a/mlxtend/frequent_patterns/tests/test_fpbase.py
+++ b/mlxtend/frequent_patterns/tests/test_fpbase.py
@@ -326,6 +326,19 @@ def test_output3(self):
             min_support=0.0,
         )
 
+    def test_output4_min_support_above_one_issue_864(self):
+        # Regression test for #864: min_support is a fraction in (0, 1].
+        # Values > 1 used to pass silently, returning an empty result;
+        # they now raise the same ValueError as min_support <= 0.
+        assert_raises(
+            ValueError,
+            "`min_support` must be a positive "
+            "number within the interval `(0, 1]`. Got 2.",
+            self.fpalgo,
+            self.df,
+            min_support=2,
+        )
+
 
 def compare_dataframes(df1, df2):
     itemsets1 = [sorted(list(i)) for i in df1["itemsets"]]