Visual Analysis of Multidimensional Clusters

• US 20120311496A1
• Filed: 05/31/2011
• Published: 12/06/2012
• Est. Priority Date: 05/31/2011
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Cao, Nan, Gotz, David H., Sun, Jimeng
• Granted Patent: US 9,342,579 B2
• Time in Patent Office: Days
• US Class Current: 715/821
• CPC Class Codes: G06F 16/287 Visualization; Browsing

Abstract

Visualization techniques are provided for a clustered multidimensional dataset. A data set is visualized by obtaining a clustering of a multidimensional dataset comprising a plurality of entities, wherein the entities are instances of a particular concept and wherein each entity comprises a plurality of features; and generating an icon for at least one of the entities, the icon having a plurality of regions, wherein each region corresponds to one of the features of the at least one entity, and wherein a size of each region is based on a value of the corresponding feature. Each icon can convey statistical measures. A stabilized Voronoi-based icon layout algorithm is optionally employed. Icons can be embedded in a visualization of the multidimensional dataset. A hierarchical encoding scheme can be employed to encode a data cluster into the icon, such as a hierarchy of cluster, feature type and entity.

Claims

1. A method for visualizing a data set, comprising:

obtaining a clustering of a multidimensional dataset comprising a plurality of entities, wherein said entities are instances of a particular concept and wherein each entity comprises a plurality of features; and

generating an icon for at least one of said entities, said icon having a plurality of regions, wherein each region corresponds to one of said features of said at least one entity, and wherein a size of each region is based on a value of said corresponding feature.

2. The method of claim 1, wherein said values of said corresponding features are normalized.

3. The method of claim 1, wherein each of said features are uniquely encoded.

4. The method of claim 3, wherein each of said features are encoded with a unique color and wherein a distribution of said colors indicates a distribution of said corresponding feature value.

5. The method of claim 1, further comprising the step of merging a plurality of said icons into a single icon.

6. The method of claim 5, wherein an outer shape of said single icon conveys one or more cluster statistical measures.

7. The method of claim 5, wherein said merging step further comprises the step of decomposing said plurality of icons into said feature values and then regrouping said feature values into said single icon.

8. The method of claim 5, further comprising the step of animating transitions of said plurality of icons into said single icon following said merging step.

9. The method of claim 1, further comprising the step of splitting at least one of said icons for a plurality of said entities into a plurality of icons.

10. The method of claim 9, further comprising the step of animating transitions of said at least one said icon into a plurality of icons following said splitting step.

11. The method of claim 1, wherein each of said icons conveys one or more statistical measures.

12. The method of claim 11, wherein an outer shape of each icon conveys said one or more statistical measures.

13. The method of claim 11, wherein one or more of a color, hash pattern or shading of each of said plurality of regions conveys said one or more statistical measures.

14. The method of claim 1, further comprising the step of employing a stabilized Voronoi-based icon layout algorithm to substantially maintain a stability of Voronoi regions when cluster changes occur.

15. The method of claim 1, further comprising the step of employing a stabilized Voronoi-based icon layout algorithm to substantially maintain a predefined order for regions within an icon that places Voronoi regions next to each other according to semantic similarities.

16. The method of claim 1, further comprising the step of embedding said icon for at least one of said entities in a visualization of said multidimensional dataset.

17. The method of claim 1, wherein said step of generating an icon further comprises the step of encoding a data cluster into said icon using a hierarchical encoding scheme.

18. The method of claim 17, wherein said hierarchical encoding scheme comprises a hierarchy of cluster, feature type and entity.

19. A system for visualizing a data set, comprising:

a memory; and

at least one processor, coupled to the memory, operative to;

obtaining a clustering of a multidimensional dataset comprising a plurality of entities, wherein said entities are instances of a particular concept and wherein each entity comprises a plurality of features; and

generating an icon for at least one of said entities, said icon having a plurality of regions, wherein each region corresponds to one of said features of said at least one entity, and wherein a size of each region is based on a value of said corresponding feature.

20. An article of manufacture for visualizing a data set, comprising a tangible machine readable recordable medium containing one or more programs which when executed implement the steps of:

obtaining a clustering of a multidimensional dataset comprising a plurality of entities, wherein said entities are instances of a particular concept and wherein each entity comprises a plurality of features; and

generating an icon for at least one of said entities, said icon having a plurality of regions, wherein each region corresponds to one of said features of said at least one entity, and wherein a size of each region is based on a value of said corresponding feature.