diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala
index d9a330f67e8dc..149e99d2f195a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala
@@ -166,6 +166,7 @@ class PowerIterationClustering private[clustering] (
     val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) {
       lit(1.0)
     } else {
+      SchemaUtils.checkNumericType(dataset.schema, $(weightCol))
       col($(weightCol)).cast(DoubleType)
     }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/PowerIterationClusteringSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/PowerIterationClusteringSuite.scala
index 55b460f1a4524..0ba3ffabb75d2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/PowerIterationClusteringSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/PowerIterationClusteringSuite.scala
@@ -145,6 +145,21 @@ class PowerIterationClusteringSuite extends SparkFunSuite
     assert(msg.contains("Similarity must be nonnegative"))
   }
 
+  test("check for invalid input types of weight") {
+    val invalidWeightData = spark.createDataFrame(Seq(
+      (0L, 1L, "a"),
+      (2L, 3L, "b")
+    )).toDF("src", "dst", "weight")
+
+    val msg = intercept[IllegalArgumentException] {
+      new PowerIterationClustering()
+        .setWeightCol("weight")
+        .assignClusters(invalidWeightData)
+    }.getMessage
+    assert(msg.contains("requirement failed: Column weight must be of type numeric" +
+      " but was actually of type string."))
+  }
+
   test("test default weight") {
     val dataWithoutWeight = data.sample(0.5, 1L).select('src, 'dst)