Method and apparatus for selecting clusterings to classify a data set
AssigneePresident and Fellows of Harvard College
InventorsGary King, Justin Grimmer
Life of this patent
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Abstract
In a computer assisted clustering method, a clustering space is generated from fixed basis partitions that embed the entire space of all possible clusterings. A lower dimensional clustering space is created from the space of all possible clusterings by isometrically embedding the space of all possible clusterings in a lower dimensional Euclidean space. This lower dimensional space is then sampled based on the number of documents in the corpus. Partitions are then developed based on the samples that tessellate the space. Finally, using clusterings representative of these tessellations, a two-dimensional representation for users to explore is created.
Claims20
A method for selecting clusterings to classify a data set, the method implemented in a data processor having a memory and a display and comprising:
- a) using the data processor to create a lower dimensional space of all possible clusterings and storing the space in the memory;
- b) using the data processor to sample the stored lower dimensional space based on a number of documents in the data set;
- c) using the data processor to build partitions based on the samples that tessellate the low-dimensional space;
- d) using the data processor to select partitions and determine remaining point locations
- e) using the data processor to project the points to a two dimensional array of points;
- and
- f) displaying points in the two-dimensional array on the display.
Apparatus for selecting clusterings to classify a data set comprising a data processor having a display and a memory with a software program therein that controls the data processor to perform the following steps:
- a) creating a lower dimensional space of all possible clusterings and storing the space in the memory;
- b) sampling the stored lower dimensional space based on a number of documents in the data set;
- c) building partitions based on the samples that tessellate the low-dimensional space;
- d) selecting partitions and determine remaining point locations
- e) projecting the points to a two-dimensional array of points;
- and
- f) displaying points in the two-dimensional array on the display.
Dependencies are inferred from the claim text.