MULTI-PASS DATA ORGANIZATION AND AUTOMATIC NAMING

• US 20110179033A1
• Filed: 03/14/2011
• Published: 07/21/2011
• Est. Priority Date: 12/28/2006
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Sarwar, Badrul M., Mount, John A.
• Granted Patent: US 8,145,638 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A method and a system to organize a data set into groups of data subsets in multiple passes using different parameters and to automatically name the groups is disclosed. For example, a data set is retrieved in accordance with a search query submitted by a user. The data set is organized into clusters based on a statistic(s) of the data set. The data set is then organized into groups of data subsets based on an attribute(s) indicated by the data set. Each of the groups are automatically named based on a property shared by data units of the group. The name(s) of a group may be mined from the data units of the group, retrieved from a structure that maps to attribute values indicated by the data units of the group, etc.

Claims

1. A method comprising:

identifying a first cluster of data items among a plurality of data items in response to a query,

each of the plurality of data items including an attribute able to have one of a plurality of values of the attribute;

subdividing the first cluster of data items into a second duster of data items and a third cluster of data items,

the subdividing of the first cluster being performed by a processor of

a machine and based on a common value of the attribute,

the common value being present in each data item within the second cluster and absent from each data item within the third cluster; and

storing the second cluster of data items as corresponding to the common attribute.

2. The method of claim 1, wherein:

the identifying of the first cluster of data items includes;

performing a statistical analysis of the plurality of data items; and

clustering at least some of the plurality of data items based on the statistical analysis to form the first cluster of data items.

3. The method of claim 2, wherein:

the performing of the statistical analysis includes processing information selected from a group consisting of;

the attribute included in each of the plurality of data items,

a number of attributes included in at least some of the plurality of data items,

a description of an item represented by one of the plurality of data items,

an amount of text in the description of the item,

a taxonomy of the item,

a probability that the item co-occurs with a further item represented by another one of the plurality of data items, and

user behavior data.

4. The method of claim 2, wherein:

the performing of the statistical analysis is based on a technique selected from a group consisting of;

k-means clustering,

fuzzy clustering, and

agglomerative clustering.

5. The method of claim 2, wherein:

the performing of the statistical analysis includes identifying a further cluster of data items among the plurality of data items,

the common value of the attribute being present in each data item within the further cluster.

6. The method of claim 2, wherein:

the performing of the statistical analysis includes identifying a further cluster of data items among the plurality of data items,

the common value of the attribute being absent from each data item within the further cluster.

7. The method of claim 1, wherein:

each data item within the second cluster represents one of a plurality of items; and

the method further comprises

determining a property shared by a majority of data items within the second cluster of data items.

8. The method of claim 7, wherein:

the determining of the property is based on the common value of the attribute present in each data item within the second cluster.

9. The method of claim 7, wherein:

the determining of the property is based on a further attribute present in a majority of data items within the second cluster.

10. The method of claim 9, wherein:

the determining of the property includes identifying a further value of the further attribute included in a majority of data items within the second cluster.

11. The method of claim 10, wherein:

the identifying of the further value is based on a data structure that includes at least one of a synonym of the further value or a term similar to the further value.

12. The method of claim 1 further comprising:

naming the second cluster.

13. The method of claim 12, wherein:

the naming of the second cluster is based on the common value of the attribute present in each data item within the second cluster.

14. The method of claim 12, wherein:

each data item within the second cluster represents one of a plurality of items; and

the naming of the second cluster is based on a property shared by a majority of the second cluster.

15. A system comprising:

a navigation module configured to access a plurality of data items,

each of the plurality of data items including an attribute able to have one of a plurality of values of the attribute; and

a processor configured by an organizing module communicatively coupled to the navigation module, the organizing module configured to;

identify a first cluster of data items among the plurality of data items in response to a query;

subdivide the first cluster of data items into a second cluster of data items and a third cluster of data items,

the subdividing of the first cluster being based on a common value of the attribute,

the common value being present in each data item within the second cluster and absent from each data item within the third cluster; and

store the second cluster of data items as corresponding to the common attribute.

16. The system of claim 15, wherein the organizing module is further configured to:

identify the first cluster of data items by performing a statistical analysis of the plurality of data items; and

cluster at least some of the plurality of data items based on the statistical analysis to form the first cluster of data items.

17. The system of claim 15, wherein:

each data item within the second cluster represents one of a plurality of items; and

the organizing module is further configured to

determine a property shared by a majority of data items within the second cluster of data items.

18. The system of claim 17, wherein the organizing module is further configured to:

determine the property based on the common value of the attribute present in each data item within the second cluster.

19. The system of claim 17, wherein the organizing module is further configured to:

determine the property based on a further attribute present in a majority of data items within the second cluster.

20. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

identifying a first cluster of data items among a plurality of data items in response to a query,

each of the plurality of data items including an attribute able to have one of a plurality of values of the attribute;

subdividing the first cluster of data items into a second cluster of data items and a third cluster of data items,

the subdividing of the first cluster being based on a common value of the attribute,

the common value being present in each data item within the second cluster and absent from each data item within the third cluster; and

storing the second cluster of data items as corresponding to the common attribute.

21. A system comprising:

means for accessing a plurality of data items,

each of the plurality of data items including an attribute able to have one of a plurality of values of the attribute; and

means for;

identifying a first cluster of data items among the plurality of data items in response to a query;

subdividing the first cluster of data items into a second cluster of data items and a third cluster of data items,

the subdividing of the first cluster being based on a common value of the attribute,

the common value being present in each data item within the second cluster and absent from each data item within the third cluster; and

storing the second cluster of data items as corresponding to the common attribute.