Anomaly Detection Data File Format

Model your data as a series of process observations or measures that are associated with an outcome of interest.  Compose each observation (row) as a common set of features (aka., independent variables or factors).  Both features and outcomes are either numerical (blood pressures, HbA1c, ages, etc.), binary (yes/no, true/false, etc.) or categorical (Gender, Service line , Floor unit, Shift, DRG, etc.).

Provide a .csv formatted file with a header row labeling each column.

1. The first column is an alphanumeric index starting with a letter that is used to uniquely identify the data row.
2. Provide additional columns of categorical or numeric data. Categorical data must not begin with a number.
3. The first row is a user supplied alphanumeric header for each column

 Sample Table:

 Sample Test Files:

Nested Grouping with Anomaly Detection

Model your data as a series of rows containing relationships composed of an end-user or group name and a group name

Provide a .csv formatted file with a header row labeling each column such as "User/Group, MemberOf".

1. The first column is an end-user or group name.
2. The second column is a group name.

 Sample Table: