A computer-program causing a computing device to perform an association measurement between a target variable and each non-target variable of a data set; select non-target variables for inclusion in a visualization based on the degree of association; perform correspondence analysis between target values of the target variable and non-target values of each selected non-target variable; order target value markers within a target row based on the degrees of closeness; order non-target value markers within each non-target row based on the degrees of closeness; determine a width of each target value marker based on a frequency of occurrence of its target value in the data set; determine a width of each non-target value marker based on a frequency of occurrence of its non-target value in the data set; and cause generation of the visualization with connection markers emanating from the target value markers and extending among the non-target value markers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions operable to cause a computing device to perform operations including: perform an association measurement between a target variable and each non-target variable of a data set to determine a degree of association between the target variable and each non-target variable; select non-target variables for inclusion in a visualization of the data set up to a maximum quantity of non-target variables for inclusion in the visualization based on the degree of association between each non-target variable and the target variable; perform correspondence analysis between target values of the target variable and non-target values of each selected non-target variable to determine degrees of closeness among the target variables and to determine degrees of closeness between the target values of the target variable and the non-target values of each of the selected non-target variables; order target value markers within a target row of the visualization based on the degrees of closeness among the target values of the target variable, wherein the target row corresponds to the target variable and each target value marker represents at least one of the target values; order non-target value markers within each non-target row of multiple non-target rows of the visualization based on the degrees of closeness between the non-target values of each of the selected non-target variables and the target values of the target variable, wherein each non-target row corresponds to one of the selected non-target variables and each non-target value marker represents a non-target value of the corresponding selected non-target variable; determine a width of each target value marker based on a frequency of occurrence of the target value represented by the target value marker in the data set; determine a width of each non-target value marker based on a frequency of occurrence of the non-target value represented by the non-target value marker in the data set; and cause generation, by circuitry, of the visualization, wherein the visualization comprises the target row and the multiple non-target rows presented as parallel rows with connection markers emanating from the target value markers and extending among the non-target value markers. 2. The computer-program product of claim 1, the computing device caused to perform operations including order the non-target rows relative to the target row based on the degree of association of the non-target values of each of the non-target variables to the target values of the target variable. 3. The computer-program product of claim 1, the computing device caused to perform operations including determine a distance between each adjacent pair of the target value markers in the target row based on degree of closeness between the target values represented by the target value markers in each adjacent pair. 4. The computer-program product of claim 1, the computing device caused to perform operations including monitor controls of the computing device for an indication of manual operation to select at least one of a variable of the data set to become the target variable or the maximum quantity of non-target variables for inclusion in the visualization. 5. The computer-program product of claim 1, the computing device caused to perform operations including receive an indication via a network from a viewing device of a selection of at least one of a variable of the data set to become the target variable or the maximum quantity of non-target variables for inclusion in the visualization. 6. The computer-program product of claim 1, the computing device caused to perform operations including generate the visualization as a parallel sets visualization wherein: the target row and the multiple non-target rows are arranged as parallel horizontal rows; at least one connection marker extends between a target value marker of the target row and a non-target value marker of a non-target row that is adjacent to the target row; and at least one other connection marker extends between a non-target value marker of one non-target row and another non-target value marker of another non-target row that is adjacent to the one non-target row. 7. The computer-program product of claim 6, the computing device caused to perform operations including: present, by circuitry, the visualization on a display; and present, by circuitry, a menu on the display, wherein the menu comprises at least one graphical control operable to alter at least one of a selection of the target variable, the maximum quantity of non-target variables, a selection of ordering of the non-target value markers within each non-target row, widths of the target value markers or the widths of the non-target value markers. 8. The computer-program product of claim 1, the computing device caused to perform operations including enable a viewing device to present the visualization on a display by transmitting preprocessed data comprising mapping data indicating ordering of the target value markers within the target row and ordering of the non-target value markers within each non-target row, and frequency data indicating frequencies of occurrence in the data set of each target value represented by each target value marker and frequencies of occurrence in the data set of each non-target value represented by each non-target value marker. 9. The computer-program product of claim 1, the computing device caused to perform operations including combine more than one of the target values into a cluster represented by a single target value marker of the target row in response to a quantity of target values of the target variable exceeding a maximum quantity of target value markers to be presented in the target row. 10. The computer-program product of claim 9, the computing device caused to perform operations including: monitor controls of the computing device for an indication of manual operation to select a specific target value combined into the cluster for separation from the cluster; and augment the target row with another target value marker representing the specific target value separately from the cluster. 11. The computer-program product of claim 9, the computing device caused to perform operations including augment the visualization with an indication of an opposite trend between a degree of representation of a specific target value and a degree of representation of the cluster in at least one non-target value, wherein the cluster comprises the specific target value. 12. The computer-program product of claim 1, the computing device caused to perform operations including augment the visualization with an indication of at least one of which target value is most overrepresented or which target value is most underrepresented in a non-target value represented by a non-target value marker of one of the non-target rows. 13. The computer-program product of claim 12, the computing device caused to perform operations including generate by circuitry, the non-target value marker to provide the indication of which target value is most overrepresented or which target value is most underrepresented in the non-target value represented by the non-target value marker. 14. The computer-program product of claim 12, the computing device caused to perform operations including reorder the non-target value markers within at least one non-target row based on degree of representation of a selected target value in each non-target value represented by the non-target value markers within the at least one non-target row. 15. A computer-implemented method comprising: performing an association measurement between a target variable and each non-target variable of a data set to determine a degree of association between the target variable and each non-target variable; selecting non-target variables for inclusion in a visualization of the data set up to a maximum quantity of non-target variables for inclusion in the visualization based on the degree of association between each non-target variable and the target variable; performing correspondence analysis between target values of the target variable and non-target values of each selected non-target variable to determine degrees of closeness among the target variables and to determine degrees of closeness between the target values of the target variable and the non-target values of each of the selected non-target variables; ordering target value markers within a target row of the visualization based on the degrees of closeness among the target values of the target variable, wherein the target row corresponds to the target variable and each target value marker represents at least one of the target values; ordering non-target value markers within each non-target row of multiple non-target rows of the visualization based on the degrees of closeness between the non-target values of each of the selected non-target variables and the target values of the target variable, wherein each non-target row corresponds to one of the selected non-target variables and each non-target value marker represents a non-target value of the corresponding selected non-target variable; determining a width of each target value marker based on frequency of occurrence of the target value represented by the target value marker in the data set; determining a width of each non-target value marker based on frequency of occurrence of the non-target value represented by the non-target value marker in the data set; and causing generation, by circuitry, of the visualization, wherein the visualization comprises the target row and the multiple non-target rows arranged as parallel rows with connection markers emanating from the target value markers and extending among the non-target value markers. 16. The computer-implemented method of claim 15, comprising ordering the non-target rows relative to the target row based on the degree of association of the non-target values of each of the non-target variables to the target values of the target variable. 17. The computer-implemented method of claim 15, comprising determining a distance between each adjacent pair of the target value markers in the target row based on degree of closeness between the target values represented by the target value markers in each adjacent pair. 18. The computer-implemented method of claim 15, comprising monitoring controls for an indication of manual operation to select at least one of a variable of the data set to become the target variable or the maximum quantity of non-target variables for inclusion in the visualization. 19. The computer-implemented method of claim 15, comprising receiving an indication via a network from a viewing device of a selection of at least one of a variable of the data set to become the target variable or the maximum quantity of non-target variables for inclusion in the visualization. 20. The computer-implemented method of claim 15, comprising generating the visualization as a parallel sets visualization wherein: the target row and the multiple non-target rows are arranged as parallel horizontal rows; at least one connection marker extends between a target value marker of the target row and a non-target value marker of a non-target row that is adjacent to the target row; and at least one other connection marker extends between a non-target value marker of one non-target row and another non-target value marker of another non-target row that is adjacent to the one non-target row. 21. The computer-implemented method of claim 20, comprising: presenting, by circuitry, the visualization on a display; and presenting, by circuitry, a menu on the display, wherein the menu comprises at least one graphical control operable to alter at least one of a selection of the target variable, the maximum quantity of non-target variables, a selection of ordering of the non-target value markers within each non-target row, widths of the target value markers or the widths of the non-target value markers. 22. The computer-implemented method of claim 15, comprising enabling a viewing device to present the visualization on a display by transmitting preprocessed data comprising mapping data indicating ordering of the target value markers within the target row and ordering of the non-target value markers within each non-target row, and frequency data indicating frequencies of occurrence in the data set of each target value represented by each target value marker and frequencies of occurrence in the data set of each non-target value represented by each non-target value marker. 23. The computer-implemented method of claim 15, comprising combining more than one of the target values into a cluster represented by a single target value marker of the target row in response to a quantity of target values of the target variable exceeding a maximum quantity of target value markers to be presented in the target row. 24. The computer-implemented method of claim 23, comprising: monitoring controls of the computing device for an indication of manual operation to select a specific target value combined into the cluster for separation from the cluster; and augmenting the target row with an another target value marker representing the specific target value separately from the cluster. 25. The computer-implemented method of claim 23, comprising augmenting the visualization with an indication of an opposite trend between a degree of representation of a specific target value and a degree of representation of the cluster in at least one non-target value, wherein the cluster comprises the specific target value. 26. The computer-implemented method of claim 15, comprising augmenting the visualization with an indication of at least one of which target value is most overrepresented or which target value is most underrepresented in a non-target value represented by a non-target value marker of one of the non-target rows. 27. The computer-implemented method of claim 26, comprising generating by circuitry, the non-value target marker to provide the indication of which target value is most overrepresented or which target value is most underrepresented in the non-target value represented by the non-target value marker. 28. The computer-implemented method of claim 26, comprising reordering the non-target value markers within at least one non-target row based on degree of representation of a selected target value in each non-target value represented by the non-target value markers within the at least one non-target row. 29. An apparatus comprising: a processor component; an association measure component for execution by the processor component to: perform an association measurement between a target variable and each non-target variable of a data set to determine a degree of association between the target variable and each non-target variable; and select non-target variables for inclusion in a visualization of the data set up to a maximum quantity of non-target variables for inclusion in the visualization based on the degree of association between each non-target variable and the target variable; a correspondence analysis component for execution by the processor component to: perform correspondence analysis between target values of the target variable and non-target values of each selected non-target variable to determine degrees of closeness among the target variables and to determine degrees of closeness between the target values of the target variable and the non-target values of each of the selected non-target variables; order target value markers within a target row of the visualization based on the degrees of closeness among the target values of the target variable, wherein the target row corresponds to the target variable and each target value marker represents at least one of the target values; and order non-target value markers within each non-target row of multiple non-target rows of the visualization based on the degrees of closeness between the non-target values of each of the selected non-target variables and the target values of the target variable, wherein each non-target row corresponds to one of the selected non-target variables and each non-target value marker represents a non-target value of the corresponding selected non-target variable; a frequency component for execution by the processor component to: determine a width of each target value marker based on frequency of occurrence of the target value represented by the target value marker in the data set; and determine a width of each non-target value marker based on frequency of occurrence of the non-target value represented by the non-target value marker in the data set; and a user interface component (UI) component for execution by the processor component to cause generation, by circuitry, of the visualization, wherein the visualization comprises the target row and the multiple non-target rows presented as parallel rows with connection markers emanating from the target value markers and extending among the non-target value markers. 30. The apparatus of claim 29, the ordering component to order the non-target rows relative to the target row based on the degree of association of the non-target values of each of the non-target variables to the target values of the target variable. 31. The apparatus of claim 29, comprising a spacing component for execution by the processing component to determine a distance between each adjacent pair of the target value markers in the target row based on degree of closeness between the target values represented by the target value markers in each adjacent pair. 32. The apparatus of claim 29, the UI component to monitor controls for an indication of manual operation to select at least one of a variable of the data set to become the target variable or the maximum quantity of non-target variables for inclusion in the visualization. 33. The apparatus of claim 29, comprising a communications component for execution by the processor component to receive an indication via a network from a viewing device of a selection of at least one of a variable of the data set to become the target variable or the maximum quantity of non-target variables for inclusion in the visualization. 34. The apparatus of claim 29, comprising the display. 35. The apparatus of claim 29, comprising a communications component for execution by the processor component to transmit to a view device preprocessed data to enable the viewing device to present the visualization on a display, wherein the preprocessed data comprises mapping data indicating ordering of the target value markers within the target row and ordering of the non-target value markers within each non-target row, and frequency data indicating frequencies of occurrence in the data set of each target value represented by each target value marker and frequencies of occurrence in the data set of each non-target value represented by each non-target value marker. 36. The apparatus of claim 29, comprising a clustering component for execution by the processor component to combine more than one of the target values into a cluster represented by a single target value marker of the target row in response to a quantity of target values of the target variable exceeding a maximum quantity of target value markers to be presented in the target row. 37. The apparatus of claim 36, wherein: the UI component monitors controls for an indication of manual operation to select a specific target value combined into the cluster for separation from the cluster; and the clustering component augments the target row with another target value marker representing the specific target value separately from the cluster. 38. The apparatus of claim 36, wherein the clustering component augments the visualization with an indication of an opposite trend between a degree of representation of a specific target value and a degree of representation of the cluster in at least one non-target value, wherein the cluster comprises the specific target value. 39. The apparatus of claim 29, comprising a representation component for execution by the processor component to augment the visualization with an indication of at least one of which target value is most overrepresented or which target value is most underrepresented in a non-target value represented by a non-target value marker of one of the non-target rows. 40. The apparatus of claim 39, the representation component to generate by circuitry, the non-value target marker to provide the indication of which target value is most overrepresented or which target value is most underrepresented in the non-target value represented by the non-target value marker. 41. The apparatus of claim 39, the representation component to reorder the non-target value markers within at least one non-target row based on degree of representation of a selected target value in each non-target value represented by the non-target value markers within the at least one non-target row.