System and method for dynamically clustering data items

1. A method for dynamically clustering data items, the method comprising:

receiving (a) a plurality of data items originating from at least two sources; (b) a plurality of distinct metadata details; (c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail indicative of the respective data items owner, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;

grading strengths of relationships between at least one of said data items and at least one of said metadata details, wherein said grading comprises applying weighting functions; and

clustering said data items into one or more clusters, based on the calculated strengths of a relationship between various data items wherein at least one of said clusters comprises related data items originating from more than one source.

2. The method of claim 1 , further comprising enabling sharing of at least one of said clusters with at least one other user or public list.

3. The method of claim 2 , further comprising receiving at least one additional data item relating to at least one of said clusters from said user or said public list.

4. The method of claim 1 , wherein the weighting functions are rule-based weighting functions.

5. The method of claim 1 wherein said clustering is based on a heuristic clustering algorithm.

6. The method of claim 1 , further comprising clustering said one or more clusters into meta clusters.

7. The method of claim 6 , further comprising enabling sharing of at least one of said meta clusters with at least one other user or public list.

8. The method of claim 6 , further comprising ranking said clusters within said meta-clusters in accordance with the relevance of said clusters.

9. The method of claim 1 , further comprising associating at least one data item with at least one additional metadata detail by utilizing information stored in a global relationships table, and wherein said grading is performed also for the at least one additional metadata detail.

10. The method of claim 1 , further comprising ranking said data items within said clusters in accordance with the relevance of said data items.

11. The method of claim 1 wherein said first data item is further associated with at least one metadata detail not associated with said second data item.

12. A system for dynamically clustering data items, the system comprising at least one processing unit configured to:

receive (a) a plurality of data items originating from at least two sources; (b) a plurality of distinct metadata details; (c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail indicative of the respective data items owner, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;

grade strengths of relationships between at least one of said data items and at least one of said metadata details, wherein said grade comprises applying weighting functions; and cluster said data items into one or more clusters, based on the calculated strengths, of a relationship between various data items wherein at least one of said clusters comprises related data items originating from more than one source.

13. The system of claim 12 , wherein said processing unit is further configured to enable sharing of at least one of said clusters with at least one other user or public list.

14. The system of claim 13 , wherein said processing unit is further configured to receive at least one additional data item relating to at least one of said clusters from said user or said public list.

15. The system of claim 12 , wherein the weighting functions are rule-based weighting functions.

16. The system of claim 12 wherein said clustering is based on a heuristic clustering algorithm.

17. The system of claim 12 , wherein said processing unit is further configured to cluster said one or more clusters into meta clusters.

18. The system of claim 17 , wherein said processing unit is further configured to enable sharing of at least one of said meta clusters with at least one other user or public list.

19. The system of claim 17 , wherein said processing unit is further configured to rank said clusters within said meta-clusters in accordance with the relevance of said clusters.

20. The system of claim 12 , wherein said processing unit is further configured to associate at least one data item with at least one additional metadata detail by utilizing information stored in a global relationships table, and wherein said grade is performed also for the at least one additional metadata detail.

21. The system of claim 12 , wherein said processing unit is further configured to rank said data items within said clusters in accordance with the relevance of said data items.

22. The system of claim 12 , wherein said first data item is further associated with at least one metadata detail not associated with said second data item.