Mechanism for unsupervised clustering
First Claim
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1. A method, comprising:
- determining, by an apparatus, cluster centers in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points, and wherein a plurality of the weight vectors represents a single non-linear cluster;
performing, by the apparatus, a first iterative process with iterations each including determining a winner weight for each data point and then updating each weight vector corresponding to the winner weight with a first neighborhood function and a corresponding first coefficient updated in a second iterative process such that the weight vectors move toward the cluster centers;
performing, by the apparatus, the second iterative process with iterations each including updating said corresponding first coefficient in a second data structure by utilizing a second neighborhood function and the winner weight determined in the first iterative process; and
determining by the apparatus, based on the second data structure, several sets of weight vectors in said lattice structure such that in each set, the weight vectors correspond to the same cluster centers of the input data points,wherein the first coefficient is limited to a range, the first neighborhood function gives only positive values, and the second neighborhood function gives negative values in a distance range between 0 and 1.
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Abstract
A computer-implemented method for determining cluster centers in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points. The method can include performing a first iterative process for iteratively updating the weight vectors such that they move toward cluster centers; performing a second iterative process for iteratively updating a second data structure utilizing results of the iterative updating of the first data structure; and determining the weight vectors that correspond to cluster centers of the input data points on the basis of the second data structure.
19 Citations
24 Claims
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1. A method, comprising:
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determining, by an apparatus, cluster centers in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points, and wherein a plurality of the weight vectors represents a single non-linear cluster; performing, by the apparatus, a first iterative process with iterations each including determining a winner weight for each data point and then updating each weight vector corresponding to the winner weight with a first neighborhood function and a corresponding first coefficient updated in a second iterative process such that the weight vectors move toward the cluster centers; performing, by the apparatus, the second iterative process with iterations each including updating said corresponding first coefficient in a second data structure by utilizing a second neighborhood function and the winner weight determined in the first iterative process; and determining by the apparatus, based on the second data structure, several sets of weight vectors in said lattice structure such that in each set, the weight vectors correspond to the same cluster centers of the input data points, wherein the first coefficient is limited to a range, the first neighborhood function gives only positive values, and the second neighborhood function gives negative values in a distance range between 0 and 1. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors causes an apparatus to perform at least the following:
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determining cluster centers in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points; and
wherein a plurality of the weight vectors represents a single non-linear cluster;performing a first iterative process with iterations each including determining a winner weight for each data point and then updating each weight vector corresponding to the winner weight with a first neighborhood function and a corresponding first coefficient updated in a second iterative process such that the weight vectors move toward the cluster centers; performing the second iterative process with iterations each including updating said corresponding first coefficient in a second data structure by utilizing a second neighborhood function and the winner weight determined in the first iterative process; and determining, based on the second data structure, several sets of weight vectors in said lattice structure such that in each set, the weight vectors correspond to the same cluster centers of the input data points, wherein the first coefficient is limited to a range, the first neighborhood function gives only positive values, and the second neighborhood function gives negative values in a distance range between 0 and 1. - View Dependent Claims (13)
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14. An apparatus, comprising:
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at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following; determine cluster centers in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points; and
wherein a plurality of the weight vectors represents a single non-linear cluster;perform a first iterative process with iterations each including determining a winner weight for each data point and then updating each weight vector corresponding to the winner weight with a first neighborhood function and a corresponding first coefficient updated in a second iterative process such that the weight vectors move toward the cluster centers; perform the second iterative process with iterations each including updating said corresponding first coefficient in a second data structure by utilizing a second neighborhood function and the winner weight determined in the first iterative process; and determine, based on the second data structure, several sets of weight vectors in said lattice structure such that in each set, the weight vectors correspond to the same cluster centers of the input data points, wherein the first coefficient is limited to a range, the first neighborhood function gives only positive values, and the second neighborhood function gives negative values in a distance range between 0 and 1. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification