Computer-implemented system and method for organizing cluster groups within a display

• US 8,792,733 B2
• Filed: 01/27/2014
• Issued: 07/29/2014
• Est. Priority Date: 02/13/2004
• Status: Expired due to Term

• Current Assignee: Nuix North America Inc. (Nuix Ltd.)
• Original Assignee: FTI Consulting Technology LLC (FTI Consulting Incorporated)
• Inventors: Evans, Lynne Marie
• Primary Examiner(s): MARIAM, DANIEL G
• Application Number:
  US14/165,557
• Publication Number:
  US 20140140631A1
• Time in Patent Office: 183 Days
• Field of Search: None
• US Class Current: 382/225
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06T 11/20 Drawing from basic elements, e.g. lines or circles
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2200/32 involving image mosaicing
• CPC Class Codes: G06V 30/416 Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors
• CPC Class Codes: H05K 999/99 dummy group
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A computer-implemented system and method for organizing cluster groups within a display is provided. Cluster groups each having one or more spines of clusters are obtained. Each cluster includes at least one document. At least one of the cluster groups is placed around a ring centrally defined in a display. Circle sectors are defined around the ring and an initial target angle is identified within each of the sectors. The at least one cluster group is positioned within one of the circle sectors at the initial target angle. A further one of the cluster groups is positioned within a different circle sector up to one of a maximum and minimum angle relative to the initial target angle for that sector.

Claims

1. A computer-implemented system for organizing cluster groups within a display, comprising:

a cluster module to obtain cluster groups each comprising one or more spines of clusters, wherein each cluster comprises at least one document;

a cluster group placement module to place at least one of the cluster groups around a ring centrally defined in a display, comprising;

a sector determination module to define circle sectors around the ring; and

an angle identification module to identify an initial target angle within each of the sectors and to position the at least one cluster group within one of the circle sectors at the initial target angle; and

a further cluster group placement module to place a further one of the cluster groups within a different circle sector up to one of a maximum and minimum angle relative to the initial target angle for that sector.

2. A system according to claim 1, further comprising:

a concept module to form concepts for the documents in the clusters by extracting concepts and terms from the documents; and

a concept score module to assign a document concept score to each of the concepts based on a frequency, concept weight, structural weight, and corpus weight of the concepts and terms.

3. A system according to claim 2, further comprising:

a cluster score module to determine for each concept in one such cluster, a cumulative score comprising the document concept scores for all the documents having that concept, to rank the cumulative scores for the concepts in that cluster, and to select one or more of the concepts as representative of the cluster.

4. A system according to claim 3, wherein each of the selected concepts is included in an initial center of the corresponding cluster and in at least one of a minimum of two documents and a predetermined percentage of the documents within that cluster.

5. A system according to claim 3, further comprising:

a spine generator to generate the spines of clusters, comprising;

a candidate spine module to select candidate spines;

a spine concept module to determine at least one candidate spine concept for each of the candidate spines;

a comparison module to compare the concepts for each cluster with each of the candidate spines; and

a cluster assignment module to assign each cluster to one of the candidate spines having a highest similarity.

6. A system according to claim 5, wherein the candidate spines are referenced by more than one cluster and are referenced by less than a predetermined percentage of clusters.

7. A system according to claim 5, further comprising:

identifying those spines that are unique from each of the other spines;

comparing each of the remaining spines with each of the unique spines; and

generating one such cluster group, comprising;

identifying the remaining spines with a highest similarity to one such unique spine; and

placing the remaining spines with the highest similarity on that unique spine as the cluster group.

8. A system according to claim 1, further comprising:

comparing another cluster group to the cluster groups placed in the display;

selecting one of the displayed cluster groups most similar to the other cluster group based on the comparison; and

placing the other cluster group along the ring at the angle of the selected cluster group.

9. A system according to claim 8, further comprising at least one of:

rotating the other cluster group along the ring when the selected cluster group and the other cluster group overlap; and

moving at least one of the other cluster group and the selected cluster group away from the ring when the rotation of the other cluster group along the ring exceeds the maximum angle or the minimum angle.

10. A system according to claim 9, further comprising:

rotating the other cluster group in an opposite direction if the other cluster group still overlaps the selected cluster group after moving away from the ring.

11. A computer-implemented method for organizing cluster groups within a display, comprising:

obtaining cluster groups each comprising one or more spines of clusters, wherein each cluster comprises at least one document;

placing at least one of the cluster groups around a ring centrally defined in a display, comprising;

defining circle sectors around the ring;

identifying an initial target angle within each of the sectors; and

positioning the at least one cluster group within one of the circle sectors at the initial target angle;

placing a further one of the cluster groups within a different circle sector up to one of a maximum and minimum angle relative to the initial target angle for that sector.

12. A method according to claim 11, further comprising:

forming concepts for the documents in the clusters by extracting concepts and terms from the documents; and

assigning a document concept score to each of the concepts based on a frequency, concept weight, structural weight, and corpus weight of the concepts and terms.

13. A method according to claim 12, further comprising:

determining for each concept in one such clusters, a cumulative score comprising the document concept scores for all the documents having that concept;

ranking the cumulative scores for the concepts in that cluster; and

selecting one or more of the concepts as representative of the cluster.

14. A method according to claim 13, wherein each of the selected concepts is included in an initial center of the corresponding cluster and in at least one of a minimum of two documents and a predetermined percentage of the documents within that cluster.

15. A method according to claim 13, further comprising:

generating the spines of clusters, comprising;

selecting candidate spines;

determining at least one candidate spine concept for each of the candidate spines;

comparing the concepts for each cluster with each of the candidate spines; and

assigning each cluster to one of the candidate spines having a highest similarity.

16. A method according to claim 15, wherein the candidate spines are referenced by more than one cluster and are referenced by less than a predetermined percentage of clusters.

17. A method according to claim 15, further comprising:

identifying those spines that are unique from each of the other spines;

comparing each of the remaining spines with each of the unique spines; and

generating one such cluster group, comprising;

identifying the remaining spines with a highest similarity to one such unique spine; and

placing the remaining spines with the highest similarity on that unique spine as the cluster group.

18. A method according to claim 11, further comprising:

comparing another cluster group to the cluster groups placed in the display;

selecting one of the displayed cluster groups most similar to the other cluster group based on the comparison; and

placing the other cluster group along the ring at the angle of the selected cluster group.

19. A method according to claim 18, further comprising at least one of:

rotating the other cluster group along the ring when the selected cluster group and the other cluster group overlap; and

moving at least one of the other cluster group and the selected cluster group away from the ring when the rotation of the other cluster group along the ring exceeds the maximum angle or the minimum angle.

20. A method according to claim 19, further comprising:

rotating the other cluster group in an opposite direction if the other cluster group still overlaps the selected cluster group after moving away from the ring.