PREFERENCE BASED CLUSTERING

1 . A computer implemented method to cluster a plurality of objects associated with a dataset, comprising:

receiving a selection of one or more criteria to cluster the objects associated with the dataset;

for the selected criteria, receiving a preference information to perform a preference-based clustering of the objects;

based on the received preference information, computing a preference degree between the objects corresponding to the selected one or more criteria;

based on the preference degree, generating a relationship matrix representing a similarity measure between the objects associated with the dataset; and

clustering the objects associated with the dataset according to the relationship matrix.

2 . The computer implemented method of claim 1 further comprising: generating a framework for clustering the objects associated with the dataset.

3 . The computer implemented method of claim 1 , wherein receiving the preference information includes:

receiving a normalized weight for the selected criteria;

receiving an indifference threshold; and

receiving a preference.

4 . The computer implemented method of claim 1 , wherein computing the preference degree includes:

for each of the selected one or more criteria, computing a corresponding uni-criterion preference degree; and

aggregating a plurality of uni-criterion preference degrees associated with the plurality of selected criteria.

5 . The computer implemented method of claim 4 , wherein a uni-criterion preference degree represents strength of a preference threshold between two or more objects associated with the dataset.

6 . The computer implemented method of claim 4 , wherein the aggregated plurality of uni-criterion preference degrees represents a universal preference threshold between the objects associated with the dataset.

7 . The computer implemented method of claim 1 , wherein generating the relationship matrix includes:

determining a preference-type associated with the preference information;

examining the preference degree between the objects, to determine a corresponding preference-type relationship; and

attributing the relationship matrix with a preference-type relationship identifier between the objects corresponding to the preference-type.

8 . The computer implemented method of claim 1 further comprising: computing the similarity measure by:

determining the objects corresponding to a preferred-to relationship and a preferred-by relationship, by examining the preference information;

comparing the preferred-to and the preferred-by relationships with one or more preference-type relationships to compute a relationship measure value between the objects; and

based upon the computed relationship measure between each object, generating a similarity pattern including the similarity measure of the plurality of objects associated with the dataset.

9 . The computer implemented method of claim 1 further comprising:

generating the similarity pattern including a plurality of nodes representing the objects associated with the dataset, and a plurality of edges representing the preference-type relationship;

attributing the one or more edges with one or more values associated with the relationship matrix; and

applying a clustering mechanism to determine one or more subsets of the nodes associated with dense connections and one or more subsets of nodes associated with sparse connections.

10 . The computer implemented method of claim 9 , wherein applying the clustering mechanism includes:

calculating betweenness for each of the plurality of edges in a preference network;

removing one or more edges with betweenenss higher than a betweenness threshold, from a list of the betweenenss of the plurality of edges; and

recalculating the betweenness for each of the remaining edges of the plurality of edges.

11 . A computer system to cluster a plurality of objects associated with a dataset, comprising:

a processor configured to read and execute instructions stored in one or more memory elements; and

the one or more memory elements storing instructions related to—

receive, from a computer generated user interface, a selection of one or more criteria to cluster the objects associated with the dataset;

for the selected criteria, receive, from a computer generated user interface, preference information to perform a preference-based clustering of the objects;

based on the received preference information, compute a preference degree between the objects corresponding to the selected criteria;

based on the preference degree, generate a relationship matrix representing a similarity measure between the objects associated with the dataset; and

cluster the objects associated with the dataset according to the relationship matrix.

12 . The computer system of claim 11 , wherein generating the relationship matrix includes:

determining a preference-type associated with the preference information;

examining the preference degree between the objects, to determine a corresponding preference-type relationship; and

attributing the relationship matrix with a preference-type relationship identifier between the objects corresponding to the preference-type.

13 . The computer system of claim 11 further comprising instructions related to: compute the similarity measure by:

determining the objects corresponding to a preferred-to relationship and a preferred-by relationship by examining the preference information;

comparing the preferred-to and the preferred-by relationships with one or more preference-type relationships to compute a relationship measure value between the objects; and

based upon the computed relationship measure between each object, generating a similarity pattern including the similarity measure of the plurality of objects associated with the dataset.

14 . The computer system of claim 11 further comprising instructions related to:

generate the similarity pattern including a plurality of nodes representing the objects associated with the dataset, and a plurality of edges representing the preference-type relationship;

attribute the one or more edges with one or more values associated with the relationship matrix; and

apply a clustering mechanism to determine one or more subsets of the nodes associated with dense connections and one or more subsets of nodes associated with sparse connections.

15 . The computer system of claim 14 , wherein applying the clustering mechanism includes:

calculating betweenness for each of the plurality of edges in a preference network;

removing one or more edges with betweenenss higher than a betweenness threshold, from a list of the betweenness of the plurality of edges; and

recalculating the betweenness for each of the remaining edges of the plurality of edges.

16 . An article of manufacture including a non-transitory computer readable storage medium to tangibly store instructions, which when executed by a computer, cause the computer to:

receive a selection of one or more criteria to cluster the objects associated with the dataset;

for the selected criteria, receive preference information to perform a preference-based clustering of the objects;

based on the received preference information, compute a preference degree between the objects corresponding to the selected criteria;

based on the preference degree, generate a relationship matrix representing a similarity measure between the objects associated with the dataset; and

cluster the objects associated with the dataset according to the relationship matrix.

17 . The article of manufacture of claim 16 , wherein generating the relationship matrix includes:

determining a preference-type associated with the preference information;

examining the preference degree between the objects, to determine a corresponding preference-type relationship; and

attributing the relationship matrix with a preference-type relationship identifier between the objects corresponding to the preference-type.

18 . The article of manufacture of claim 16 further cause the computer to: compute the similarity measure by:

determining the objects corresponding to a preferred-to relationship and a preferred-by relationship by examining the preference information;

comparing the preferred-to and the preferred-by relationships with one or more preference-type relationships to compute a relationship measure value between the objects; and

based upon the computed relationship measure between each object, generating a similarity pattern including the similarity measure of the plurality of objects associated with the dataset.

19 . The article of manufacture of claim 16 further cause the computer to:

generate the similarity pattern including a plurality of nodes representing the objects associated with the dataset, and a plurality of edges representing the preference-type relationship;

attribute the one or more edges with one or more values associated with the relationship matrix; and

apply a clustering mechanism to determine one or more subsets of the nodes associated with dense connections and one or more subsets of nodes associated with sparse connections.

20 . The article of manufacture of claim 19 , wherein applying the clustering mechanism includes:

calculating betweenness for each of the plurality of edges in a preference network;

removing one or more edges with a betweenenss higher than a betweenness threshold, from a list of the betweenenss of the plurality of edges; and

recalculating the betweenness of the edges affected by the removal for the edges.