CA2556362C - Arranging concept clusters in thematic neighborhood relationships in a two-dimensional display - Google Patents
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Abstract
A set of clusters (50) is selected from a concept space. The concept space includes clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47). A theme in each of a plurality of the clusters (50) is identified. Each theme includes at least one such concept (53) ranked within the cluster (50). Unique candidate spines (55) is logically formed. Each candidate spine (55) includes clusters (50) commonly sharing at least one such concept (54). The clusters (50) are assigned to one such candidate spine (55) having a substantially best fit. Each such sufficiently unique best fit candidate spine (56) is identified and placed in a visual display space (43). Each non-identified best fit candidate spine (56) is placed in the visual display space (43) relative to an anchor cluster (60) on one such identified best fit candidate spine (56).
Claims (36)
1. A system (34) for arranging concept clusters (53) in thematic neighborhood relationships in a two-dimensional visual display space (43), comprising:
a set of clusters (50) selected from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
a theme generator (41) to identify a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50); and a spine placer (42), comprising:
a candidate spine selector to logically form a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54);
a candidate spine assigner to assign one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
a best fit candidate spine placer to identify each best fit candidate spine (56) sufficiently unique from each other best fit candidate spine (56) and to place at least one of the identified best fit candidate spines (56) in a visual display space (43) by ordering the best fit candidate spines by a number of the clusters assigned to each spine, comparing at least one of the best fit candidate spines with one or more unique candidate spines previously selected, and selecting the best fit candidate spine for placement in the visual display space when sufficiently unique from the previously selected unique candidate spines; and a remaining candidate spine placer to place each non-identified best fit candidate spine (56) in the visual display space (43) next to an anchor cluster (60) on one identified best fit candidate spine (56), comprising:
a further candidate spine selector to select one of the non-identified best fit candidate spines and to identify the anchor cluster;
a spine grafter to identify one of the identified best fit candidate spines having a cluster most similar to the anchor cluster and to graft the non-identified best fit candidate spine along a vector defined from a center of the anchor cluster; and an angle selector to determine whether the vector forms a maximum line segment and to change an angle of the vector when the maximum line segment is formed.
a set of clusters (50) selected from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
a theme generator (41) to identify a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50); and a spine placer (42), comprising:
a candidate spine selector to logically form a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54);
a candidate spine assigner to assign one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
a best fit candidate spine placer to identify each best fit candidate spine (56) sufficiently unique from each other best fit candidate spine (56) and to place at least one of the identified best fit candidate spines (56) in a visual display space (43) by ordering the best fit candidate spines by a number of the clusters assigned to each spine, comparing at least one of the best fit candidate spines with one or more unique candidate spines previously selected, and selecting the best fit candidate spine for placement in the visual display space when sufficiently unique from the previously selected unique candidate spines; and a remaining candidate spine placer to place each non-identified best fit candidate spine (56) in the visual display space (43) next to an anchor cluster (60) on one identified best fit candidate spine (56), comprising:
a further candidate spine selector to select one of the non-identified best fit candidate spines and to identify the anchor cluster;
a spine grafter to identify one of the identified best fit candidate spines having a cluster most similar to the anchor cluster and to graft the non-identified best fit candidate spine along a vector defined from a center of the anchor cluster; and an angle selector to determine whether the vector forms a maximum line segment and to change an angle of the vector when the maximum line segment is formed.
2. A system (34) according to Claim 1, further comprising:
a concept scorer to determine a cumulative score (51) for one or more of the concepts (53) for each of the plurality of clusters (50); and a concept ranker to rank the concepts (53) by the cumulative score (51) in at least one of descending and ascending order.
a concept scorer to determine a cumulative score (51) for one or more of the concepts (53) for each of the plurality of clusters (50); and a concept ranker to rank the concepts (53) by the cumulative score (51) in at least one of descending and ascending order.
3. A system (34) according to Claim 1, further comprising:
a concept evaluator to evaluate each of the plurality of concepts (53) against an acceptance criteria to qualify as the theme of the cluster (50).
a concept evaluator to evaluate each of the plurality of concepts (53) against an acceptance criteria to qualify as the theme of the cluster (50).
4. A system (34) according to Claim 3, wherein the acceptance criteria comprises at least one of being contained in a seed theme of a cluster (50) and being contained in a predetermined minimum of the documents (49).
5. A system (34) according to Claim 1, further comprising:
a candidate spine evaluator to evaluate such candidate spine (55) against an acceptance criteria.
a candidate spine evaluator to evaluate such candidate spine (55) against an acceptance criteria.
6. A system (34) according to Claim 5, wherein the acceptance criteria comprises the at least one such concept (54) being contained in at least one of a plurality of the plurality of clusters (50) and within a predetermined maximum of the plurality of clusters (50).
7. A system (34) according to Claim 1, further comprising:
a spine fit evaluator to determine a spine fit between the concept (54) in each such cluster (50) and the at least one theme commonly shared by the clusters (50) in each of the candidate spines (55); and a spine fit selector to select the spine fit comprising a maximum spine fit as the substantially best fit.
a spine fit evaluator to determine a spine fit between the concept (54) in each such cluster (50) and the at least one theme commonly shared by the clusters (50) in each of the candidate spines (55); and a spine fit selector to select the spine fit comprising a maximum spine fit as the substantially best fit.
8. A system (34) according to Claim 7, wherein the spine fit is calculated in accordance to an equation:
where popularity is defined as a number of clusters (50) containing each such concept (54) in the candidate spine (55), rank is defined as a rank of the candidate spine concept (54), and scale is defined as a bias factor.
where popularity is defined as a number of clusters (50) containing each such concept (54) in the candidate spine (55), rank is defined as a rank of the candidate spine concept (54), and scale is defined as a bias factor.
9. A system (34) according to Claim 1, wherein each such best fit candidate spine (56) containing only one such cluster (50) is discarded.
10. A system (34) according to Claim 1, further comprising:
a spine concept score vector (57) generated for each such best fit candidate spine (56);
and a similarity evaluator to evaluate a similarity between the best fit candidate spine (56) and each other such other such best fit candidate spine (56).
a spine concept score vector (57) generated for each such best fit candidate spine (56);
and a similarity evaluator to evaluate a similarity between the best fit candidate spine (56) and each other such other such best fit candidate spine (56).
11. A system (34) according to Claim 10, further comprising:
a concept score aggregator to aggregate a concept score (51) for each such concept (54) contained in each cluster (50) in the best fit candidate spine (56); and a concept score normalizer to normalize each aggregated concept score (51).
a concept score aggregator to aggregate a concept score (51) for each such concept (54) contained in each cluster (50) in the best fit candidate spine (56); and a concept score normalizer to normalize each aggregated concept score (51).
12. A system (34) according to Claim 10, wherein the similarity is calculated as a cosine over the spine concept score vectors (57).
13. A system (34) according to Claim 1, further comprising:
a similarity identifier to determine a similarity between at least one anchor cluster candidate (50) and at least one such cluster (50) in a non-identified best fit candidate spine (56), and to identify the at least one such anchor cluster (60) candidate with acceptable similarity as the anchor cluster (60).
a similarity identifier to determine a similarity between at least one anchor cluster candidate (50) and at least one such cluster (50) in a non-identified best fit candidate spine (56), and to identify the at least one such anchor cluster (60) candidate with acceptable similarity as the anchor cluster (60).
14. A system (34) according to Claim 13, wherein the similarity is calculated as a cosine over the anchor cluster (60) candidate and one such cluster (50) in the spine (56).
15. A system (34) according to Claim 1, wherein the placement of the non-identified best fit candidate spine (56) is adjusted if overlapping with at least one other cluster (50) already placed.
16. A system (34) according to Claim 1, wherein the non-identified best fit candidate spine (56) is labeled as containing at least one anchor cluster candidate (50) following placement.
17. A system (34) according to Claim 1, wherein the non-identified best fit candidate spine (56) is placed along a vector originating from the anchor cluster (60) with an angle comprising at least one of where 0 <= .sigma. < II.
18. A method (100) for arranging concept clusters (53) in thematic neighborhood relationships in a two-dimensional visual display space (43), comprising:
selecting a set of clusters (50) from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
identifying (110) a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50);
logically forming (120) a plurality of candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54) and assigning one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
identifying (140) each best fit candidate spine (56) sufficiently unique from each other best fit candidate spine (56) and placing at least one of the identified best fit candidate spines (56) in a visual display space (43), comprising:
ordering the best fit candidate spines by a number of the clusters assigned to each spine; and comparing at least one of the best fit candidate spines with one or more unique candidate spines previously selected and selecting the best fit candidate spine for placement in the visual display space when sufficiently unique from the previously selected unique candidate spines; and placing (160) each non-identified best fit candidate spine (56) in the visual display space (43) next to an anchor cluster (60) on one such identified best fit candidate spine (56), comprising:
selecting at least one of the non-identified best fit candidate spines and identifying the anchor cluster;
identifying one of the identified best fit candidate spines having a cluster most similar to the anchor cluster and grafting the non-identified best fit candidate along a vector defined from a center of the anchor cluster; and determining whether the vector forms a maximum line segment and changing an angle of the vector when the maximum line segment is formed.
selecting a set of clusters (50) from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
identifying (110) a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50);
logically forming (120) a plurality of candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54) and assigning one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
identifying (140) each best fit candidate spine (56) sufficiently unique from each other best fit candidate spine (56) and placing at least one of the identified best fit candidate spines (56) in a visual display space (43), comprising:
ordering the best fit candidate spines by a number of the clusters assigned to each spine; and comparing at least one of the best fit candidate spines with one or more unique candidate spines previously selected and selecting the best fit candidate spine for placement in the visual display space when sufficiently unique from the previously selected unique candidate spines; and placing (160) each non-identified best fit candidate spine (56) in the visual display space (43) next to an anchor cluster (60) on one such identified best fit candidate spine (56), comprising:
selecting at least one of the non-identified best fit candidate spines and identifying the anchor cluster;
identifying one of the identified best fit candidate spines having a cluster most similar to the anchor cluster and grafting the non-identified best fit candidate along a vector defined from a center of the anchor cluster; and determining whether the vector forms a maximum line segment and changing an angle of the vector when the maximum line segment is formed.
19. A method (100) according to Claim 18, further comprising:
determining a cumulative score (51) for one or more of the concepts (53) for each of the plurality of clusters (50); and ranking the concepts (53) by the cumulative score (51) in at least one of descending and ascending order.
determining a cumulative score (51) for one or more of the concepts (53) for each of the plurality of clusters (50); and ranking the concepts (53) by the cumulative score (51) in at least one of descending and ascending order.
20. A method (100) according to Claim 18, further comprising:
evaluating each of the plurality of concepts (53) against an acceptance criteria to qualify as the theme of the cluster (50).
evaluating each of the plurality of concepts (53) against an acceptance criteria to qualify as the theme of the cluster (50).
21. A method (100) according to Claim 20, wherein the acceptance criteria comprises at least one of being contained in a seed theme of a cluster (50) and being contained in a predetermined minimum of the documents (49).
22. A method (100) according to Claim 18, further comprising:
evaluating such candidate spine (55) against an acceptance criteria.
evaluating such candidate spine (55) against an acceptance criteria.
23. A method (100) according to Claim 22, wherein the acceptance criteria comprises the at least one such concept (54) being contained in at least one of a plurality of the plurality of clusters (50) and within a predetermined maximum of the plurality of clusters (50).
24. A method (100) according to Claim 18, further comprising:
determining a spine fit between the concept (54) in each such cluster (50) and the at least one theme commonly shared by the clusters (50) in each of the candidate spines (55); and selecting the spine fit comprising a maximum spine fit as the substantially best fit.
determining a spine fit between the concept (54) in each such cluster (50) and the at least one theme commonly shared by the clusters (50) in each of the candidate spines (55); and selecting the spine fit comprising a maximum spine fit as the substantially best fit.
25. A method (100) according to Claim 24, wherein the spine fit is calculated in accordance to an equation:
where popularity is defined as a number of clusters (50) containing each such concept (54) in the candidate spine (55), rank is defined as a rank of the candidate spine concept (54), and scale is defined as a bias factor.
where popularity is defined as a number of clusters (50) containing each such concept (54) in the candidate spine (55), rank is defined as a rank of the candidate spine concept (54), and scale is defined as a bias factor.
26. A method (100) according to Claim 18, further comprising:
discarding each such best fit candidate spine (56) containing only one such cluster (50).
discarding each such best fit candidate spine (56) containing only one such cluster (50).
27. A method (100) according to Claim 18, further comprising:
generating a spine concept score vector (57) for each such best fit candidate spine (56);
and evaluating a similarity between the best fit candidate spine (56) and each other such other such best fit candidate spine (56).
generating a spine concept score vector (57) for each such best fit candidate spine (56);
and evaluating a similarity between the best fit candidate spine (56) and each other such other such best fit candidate spine (56).
28. A method (100) according to Claim 27, further comprising:
aggregating a concept score (51) for each such concept (54) contained in each cluster (50) in the best fit candidate spine (56); and normalizing each aggregated concept score (51).
aggregating a concept score (51) for each such concept (54) contained in each cluster (50) in the best fit candidate spine (56); and normalizing each aggregated concept score (51).
29. A method (100) according to Claim 27, further comprising:
calculating the similarity as a cosine over the spine concept score vectors (57).
calculating the similarity as a cosine over the spine concept score vectors (57).
30. A method (100) according to Claim 18, further comprising:
determining a similarity between at least one anchor cluster candidate (50) and at least one such cluster (50) in a non-identified best fit candidate spine (56); and identifying the at least one such anchor cluster (60) candidate with acceptable similarity as the anchor cluster (60).
determining a similarity between at least one anchor cluster candidate (50) and at least one such cluster (50) in a non-identified best fit candidate spine (56); and identifying the at least one such anchor cluster (60) candidate with acceptable similarity as the anchor cluster (60).
31. A method (100) according to Claim 30, further comprising:
calculating the similarity as a cosine over the anchor cluster (60) candidate and one such cluster (50) in the spine (56).
calculating the similarity as a cosine over the anchor cluster (60) candidate and one such cluster (50) in the spine (56).
32. A method (100) according to Claim 18, further comprising:
adjusting placement of the non-identified best fit candidate spine (56) if overlapping with at least one other cluster (50) already placed.
adjusting placement of the non-identified best fit candidate spine (56) if overlapping with at least one other cluster (50) already placed.
33. A method (100) according to Claim 18, further comprising:
labeling the non-identified best fit candidate spine (56) as containing at least one anchor cluster candidate (50) following placement.
labeling the non-identified best fit candidate spine (56) as containing at least one anchor cluster candidate (50) following placement.
34. A method (100) according to Claim 18, further comprising:
placing the non-identified best fit candidate spine (56) along a vector originating from the anchor cluster (60) with an angle comprising at least one of where 0 <= .sigma.< II.
placing the non-identified best fit candidate spine (56) along a vector originating from the anchor cluster (60) with an angle comprising at least one of where 0 <= .sigma.< II.
35. A computer-readable storage medium holding code for performing the method (100) according to Claim 18.
36. An apparatus for arranging concept clusters (53) in thematic neighborhood relationships in a two-dimensional visual display space (43), comprising:
means for selecting a set of clusters (50) from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
means for identifying a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50);
means for logically forming a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54) and means for assigning one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
means for identifying each best fit candidate spine (56) sufficiently unique from each other best fit candidate spine (56) and means for placing at least one of the identified best fit candidate spines (56) in a visual display space (43), comprising:
means for ordering the best fit candidate spines by a number of the clusters assigned to each spine; and means for comparing at least one of the best fit candidate spines with one or more unique candidate spines previously selected and means for selecting the best fit candidate spine for placement in the visual display space when sufficiently unique from the previously selected unique candidate spines; and means for placing each non-identified best fit candidate spine (56) in the visual display space (43) next to an anchor cluster (60) on one such identified best fit candidate spine (56), comprising:
means for selecting at least one of the non-identified best fit candidate spines and means for identifying the anchor cluster;
means for identifying one of the identified best fit candidate spines having a cluster most similar to the anchor cluster and means for grafting the non-identified best fit candidate along a vector defined from a center of the anchor cluster; and means for determining whether the vector forms a maximum line segment and means for changing an angle of the vector when the maximum line segment is formed.
means for selecting a set of clusters (50) from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
means for identifying a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50);
means for logically forming a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54) and means for assigning one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
means for identifying each best fit candidate spine (56) sufficiently unique from each other best fit candidate spine (56) and means for placing at least one of the identified best fit candidate spines (56) in a visual display space (43), comprising:
means for ordering the best fit candidate spines by a number of the clusters assigned to each spine; and means for comparing at least one of the best fit candidate spines with one or more unique candidate spines previously selected and means for selecting the best fit candidate spine for placement in the visual display space when sufficiently unique from the previously selected unique candidate spines; and means for placing each non-identified best fit candidate spine (56) in the visual display space (43) next to an anchor cluster (60) on one such identified best fit candidate spine (56), comprising:
means for selecting at least one of the non-identified best fit candidate spines and means for identifying the anchor cluster;
means for identifying one of the identified best fit candidate spines having a cluster most similar to the anchor cluster and means for grafting the non-identified best fit candidate along a vector defined from a center of the anchor cluster; and means for determining whether the vector forms a maximum line segment and means for changing an angle of the vector when the maximum line segment is formed.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
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US10/778,416 | 2004-02-13 | ||
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