The plots

• Table view:
  A table view of the data matrix, with a color tag attached to each row that shows its membership in partitional clusters. Points can be selected by a mouse dragged to cover the corresponding rows, and highlighted by changing the background color of the rows.
• Histogram view:
  A histogram plot that can be reconfigured to focus on any single feature. This shows one-dimensional projections of the data set, divided into a (choosable) number of groups. The groups give a partitional structure of the data set in this subspace. One can select one or more clusters by drawing an interval with a mouse. Partitions from other sources are shown by coloring each bar in heights proportional to corresponding fractions of members in each cluster. A selected subset is highlighted and can be tracked in another subspace by reconfiguring the plot to another feature, or broadcasted to other displays.
• Scatter plot:
  A scatter plot that displays a two-dimensional projection of the data, where the X and Y axes can be chosen to be any of the feature dimensions. Regions in the projection plane can be selected by drawing boxes or irregular regions with a mouse. The selected points are highlighted and the same subset can be tracked as the plot is reconfigured to show a different pair of features, or broadcasted to to other plots.
• Feature vector plot:
  A feature vector plot is also known as a plot of parallel coordinates or profiles. This plot shows the projection of data on a multi-dimensional subspace by plotting the value of every feature against the index of that feature in the subspace. That is, a point projected on a subspace of m dimensions as (z1, ..., zm) is shown as a curve with nodes marked at (i,zi) for each i in [1,m]. This plot is a natural display for vectors such as a spectrum represented as intensities in each channel, or a time series that has values at each time step. Vectors of measurements on incomparable scales need to be first standardized so that each component has mean 0 and standard deviation 1. Data can be selected and broadcasted from this plot by drawing intervals in each feature dimension and composing unions or intersections of such intervals. Highlights and partitions are shown by coloring the curves. The plot can be reconfigured to show vectors in different subspaces with selections preserved. Feature vectors are defined by format statements "format vec vecname x1 x2 x3 ... ". The vectors of the same name that are associated with different data entries are assumed to share the same set of indices.

Operations on the plots

Data in every plot can be selected by mouse operations that draw one or more boxes enclosing the selected region. This operation is available by clicking the rectangle icon in the right tool bar. Additional shapes such as an irregular region or Bezier curve are applicable in scatter plots. Intersection or union of multiple selected regions can be formed by toggling the intersection/union icon in the right tool bar.

Some plots have choosable actions built in, such as changing the axes, or stepping through each data entry. These actions can be triggered by pressing the circle icons in each plot. Pressing the solid circle triggers or stops a continuous action. Broken circles are for one step of the action in the forward or backward direction.

Operations with selected data

Data selected from each plot can be colored, shown in isolation in one of the four basic views, or broadcast to other plots.