US 8,407,164 B2 · Data classification and hierarchical clustering

Abstract

Apparatus, systems, and methods can operate to provide efficient data clustering, data classification, and data compression. A method comprises training set of training instances can be processed to select a subset of size-1 patterns, initialize a weight of each size-1 pattern, include the size-1 patterns in classes in a model associated with the training set, and then include a set of top-k size-2 patterns in a way that provides an effective balance between local, class, and global significance patterns. A method comprises processing a dataset to compute an overall significance value of each size-2 pattern in each instance in the dataset, sort the size-2 patterns, and select the top-k size-2 patterns to be represented in clusters, which can be refined into a clustered hierarchy. A method comprises creating an uncompressed bitmap, reordering the bitmap, and compressing the bitmap. Additional apparatus, systems, and methods are disclosed.

Claims30

Dependent claims

A computer-implemented method comprising:

using a computer comprising a processor to perform:
initializing a model, the model including a plurality of classes;
selecting subsets of patterns from a set of available patterns in a training instance selected from a training set of training instances, the selecting subsets including selecting a subset of size-1 patterns and selecting a subset of size-2 patterns;
initializing a weight of each size-1 pattern in the subset of size-1 patterns;
including each size-1 pattern in the subset of size-1 patterns in each class in the plurality of classes in the model;
calculating an overall significance value of each size-2 pattern in the training instance;
sorting the size-2 patterns using the overall significance;
selecting the highest k sorted size-2 patterns;
initializing a weight of each selected highest k size-2 pattern;
adjusting the weights on the size-1 and size-2 patterns;
and
presenting the model organized with the plurality of classes, each class including the size-1 patterns, the highest k size-2 patterns, and the weights of the size-1 and size-2 patterns.

9 more elements

A system comprising:

a memory;
and
a control module coupled to the memory, the control module comprising:
a first initialization module to initialize a model, the model including a plurality of classes;
a first selection module to select subsets of patterns from a set of available patterns in a training instance selected from a training set of training instances, the selecting subsets including selecting a subset of size-1 patterns and selecting a subset of size-2 patterns;
a second initialization module to initialize a weight of each size-1 pattern in the subset of size-1 patterns;
an organization module to include each size-1 pattern in the subset of size-1 patterns in each class in the plurality of classes in the model;
a first calculation module to calculate an overall significance value of each size-2 pattern in the training instance;
a sorting module to sort the size-2 patterns using the overall significance;
a second selection module to select the highest k sorted size-2 patterns;
a third initialization module to initialize a weight of each selected highest k size-2 pattern;
an adjustment module to adjust the weights on the size-1 and size-2 patterns;
and
a presentation module to present the model organized with the plurality of classes, each class including the size-1 patterns, the highest k size-2 patterns, and the weights of the size-1 and size-2 patterns.

11 more elements

Dependencies are inferred from the claim text.

Data classification and hierarchical clustering

Life of this patent

Data classification and hierarchical clustering

Life of this patent

Abstract

Claims30