@@ -88,11 +88,12 @@ class PowerIterationClustering private[clustering] (
8888 /**
8989 * Run the PIC algorithm.
9090 *
91- * @param similarities an RDD of (i, j, s_ij_) tuples representing the affinity matrix, which is
92- * the matrix A in the PIC paper. The similarity s_ij_ must be nonnegative.
93- * This is a symmetric matrix and hence s_ij_ = s_ji_. For any (i, j) with
94- * nonzero similarity, there should be either (i, j, s_ij_) or (j, i, s_ji_)
95- * in the input. Tuples with i = j are ignored, because we assume s_ij_ = 0.0.
91+ * @param similarities an RDD of (i, j, s,,ij,,) tuples representing the affinity matrix, which is
92+ * the matrix A in the PIC paper. The similarity s,,ij,, must be nonnegative.
93+ * This is a symmetric matrix and hence s,,ij,, = s,,ji,,. For any (i, j) with
94+ * nonzero similarity, there should be either (i, j, s,,ij,,) or
95+ * (j, i, s,,ji,,) in the input. Tuples with i = j are ignored, because we
96+ * assume s,,ij,, = 0.0.
9697 *
9798 * @return a [[PowerIterationClusteringModel ]] that contains the clustering result
9899 */
@@ -109,7 +110,7 @@ class PowerIterationClustering private[clustering] (
109110 * Runs the PIC algorithm.
110111 *
111112 * @param w The normalized affinity matrix, which is the matrix W in the PIC paper with
112- * w_ij_ = a_ij_ / d_ii_ as its edge properties and the initial vector of the power
113+ * w,,ij,, = a,,ij,, / d,,ii,, as its edge properties and the initial vector of the power
113114 * iteration as its vertex properties.
114115 */
115116 private def pic (w : Graph [Double , Double ]): PowerIterationClusteringModel = {
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