Contents
- Index

**TOOLS > DISSIMILARITIES**

** **

**PURPOSE **Compute dissimilarities among rows or columns of a matrix using one of various measures.

**DESCRIPTION **Given a matrix with n rows and m columns, the program computes either an n-by-n matrix of dissimilarities among the rows, or an m-by-m matrix of dissimilarities among the columns.

**PARAMETERS**

* ***Input dataset:**

Name of file containing matrix to be analyzed. Data type: Matrix.

* ***Measure of profile similarity**: (Default = 'EUCLIDEAN')

Choices are:

**Euclidean**

Euclidean distance: SQRT(S(xi-yi)^2) . When missing values are present, the computed distance is multiplied by n/m where n is the size of the vectors and m is the number of non-missing values.

**Manhattan**

City-block distance: S abs(xi-yi) When missing values are present, the computed distance is multiplied by n/m where n is the size of the vectors and m is the number of non-missing values.

**Normed SSD**

** **Normed sum of squared differences: S(xi-yi)^2/ Sxi^2Syi^2

**Non-Matches**

** **Proportion of cases in which xi does not equal yi for all i.

**Positive Non-Matches**

Proportion of cases in which xi does not equal yi given that either xi > 0 or yi > 0 or both.

* ***Compute dissimilarities among Rows or Cols **(Default = COLUMNS)

If **Rows**, an n-by-n dissimilarity matrix representing the dissimilarity between each pair of rows is computed. If **Columns** an m-by-m dissimilarity matrix is computed representing the dissimilarity between each pair of columns.

* ***(For sq. mats) Diagonal valid*** *(Default = YES)

If No, values along the main diagonal are treated as though they were missing.

* ***Output dataset***:*(Default = Dissimilarities)

Name of dataset to contain output dissimilarity matrix.

**LOG FILE **Dissimilarity matrix.

**TIMING **O(N^3).

**COMMENTS **Missing values are ignored.

**REFERENCES **None.