AUTOMATIC QUERY CLUSTERING

• US 20080021896A1
• Filed: 08/13/2007
• Published: 01/24/2008
• Est. Priority Date: 01/26/2004
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: MacLaurin, Matthew, Rashid, Richard, Cheng, Lili, Turski, Andrzej
• Application Number:
  US11/838,126
• Publication Number:
  US 20080021896A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/148 File search processing
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation

Abstract

The present invention relates to a system and methodology for automatic clusterization and display of data items in a local or remote database system. Such clusterization can be based on properties associated with the data items such as a type, location, people, date, time, user-defined, and so forth, wherein an initial property may be employed to form a first level of clusterization and a subsequent property may be automatically determined to form an optimized clusterization from which to find and retrieve desired information. A computerized interface for organizing and retrieving data is provided. The interface includes a property analyzer to determine an item distribution for at least two cluster properties and an organizer that forms new clusters based in part on the item distribution.

Claims

1. A system for automatically clustering query results, comprising:

means for retrieving properties of a plurality of items;

means for determining a score for the plurality of items based upon the properties; and

means for automatically clustering data associated with the items based upon the determined score.

2. A method for automatic query clustering, comprising:

associating one or more properties with a plurality of data items;

determining a distribution for the data items based upon the properties; and

automatically clustering the data items based upon the determined distribution.

3. The method of claim 2, the distribution is determined from at least one of the following equations:

score=n_items_cluster1*n_items_cluster2* . . .
score=(N_total)!/((n_items_cluster1)!*(n_items_cluster2)!* . . . )

4. The method of claim 2, further comprising processing N items and M properties.

5. The method of claim 4, further comprising at least one of initializing M hash tables, iterating through N items and, for each item, iterating through M properties.

6. The method of claim 5, further comprising calculating a hash value for each property.

7. The method of claim 6, further comprising calculating a clusterization score for each property using data from an associated hash table.

8. The method of claim 2, further comprising automatically organizing clusters based upon a predetermined threshold.

9. The method of claim 8, further comprising suggesting alternative cluster grouping.

10. The method of claim 8, further comprising organizing clusters based upon user-defined properties.