SPECTRAL CLUSTERING FOR MULTI-TYPE RELATIONAL DATA
First Claim
Patent Images
1. A method comprising:
- (a) embodying data structures on a computer readable medium, the data structures comprising;
(i) data objects, each data object including a vector with a plurality of elements of a corresponding respective type;
(ii) feature matrices, there being a feature matrix corresponding with each data object;
(iii) relationship matrices describing pairwise relationships between the data objects; and
(iv) weighting matrices;
(b) effecting a spectral clustering algorithm on at least one data processing device, the spectral clustering algorithm comprising;
(i) assigning, to each of a plurality of vigorous cluster indicator matrices, leading eigenvectors derived from a formula that is a function of the feature matrices, the relationship matrices, the weight matrices and at least one cluster indicator matrix other than the one receiving the assigning;
(ii) iteratively improving the vigorous cluster indicator matrices; and
(iii) transforming the vigorous cluster indicator matrices into a set of cluster indicator matrices comprising a cluster indicator matrix for each data object; and
(c) recognizing a clustering of the elements within a data object based on the cluster indicator matrices.
3 Assignments
0 Petitions
Accused Products
Abstract
A general model is provided which provides collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Under this model, a spectral relational clustering algorithm is provided to cluster multiple types of interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects.
-
Citations
24 Claims
-
1. A method comprising:
-
(a) embodying data structures on a computer readable medium, the data structures comprising; (i) data objects, each data object including a vector with a plurality of elements of a corresponding respective type; (ii) feature matrices, there being a feature matrix corresponding with each data object; (iii) relationship matrices describing pairwise relationships between the data objects; and (iv) weighting matrices; (b) effecting a spectral clustering algorithm on at least one data processing device, the spectral clustering algorithm comprising; (i) assigning, to each of a plurality of vigorous cluster indicator matrices, leading eigenvectors derived from a formula that is a function of the feature matrices, the relationship matrices, the weight matrices and at least one cluster indicator matrix other than the one receiving the assigning; (ii) iteratively improving the vigorous cluster indicator matrices; and (iii) transforming the vigorous cluster indicator matrices into a set of cluster indicator matrices comprising a cluster indicator matrix for each data object; and (c) recognizing a clustering of the elements within a data object based on the cluster indicator matrices. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
-
-
13. A method of indicating clusters, comprising the steps of:
-
(a) initializing a set of matrices with orthonormal matrices representing multi-type objects, each object having a respective relation with other objects and a set of features; (b) automatically iteratively processing a symmetric matrix comprising a set of weights, which maximizes an objective function of the orthonormal matrices, relations and the features, and then updating each orthonormal matrix by the leading eigenvectors of the symmetric matrix; and (c) transforming each of a set of resulting updated orthonormal matrix into a cluster indicator matrix. - View Dependent Claims (14, 15, 16, 17)
-
-
18. A method of clustering data, comprising the steps of:
-
(a) providing a set of first matrices relating to at least three types of objects, each object having a set of features and a set of respective relations with respect to other objects; (b) automatically generating a second matrix comprising a set of values, which maximizes an objective function of the sets of first matrices, features and relations; and (c) automatically transforming each second matrix into a cluster indicator matrix. - View Dependent Claims (19, 20, 21)
-
-
22. A computer implemented method for reducing dimensionality of data objects in a multi-type relational database, the method comprising executing operations in at least one data processing device, the operations comprising:
-
(a) initializing a set of matrices, which are precursors to cluster indicator matrices; (b) iteratively processing each matrix as the leading eigenvectors of a linear combination of at least one of the following terms;
(i) at least one feature matrix product that is a feature matrix multiplied by its transpose; and
(ii) at least one relationship/cluster matrix that is a joint function of a relationship matrix, a precursor to a cluster matrix, the transpose of the precursor to the cluster matrix; and
the transpose of the relationship matrix. - View Dependent Claims (23, 24)
-
Specification