Identifying similarities within large collections of unstructured data
View Patent ↗A technique for determining when documents stored in digital format in a data processing system are similar. A method compares a sparse representation of two or more documents by breaking the documents into “chunks” of data of predefined sizes. Selected subsets of the chunks are determined as being representative of data in the documents and coefficients are developed to represent such chunks. Coefficients are then combined into coefficient clusters containing coefficients that are similar according to a predetermined similarity metric. The degree of similarity between documents is then evaluated by counting clusters into which chunks of similar documents fall.
1. A method for determining if a first and second document stored in a digital format in a data processing system are similar by comparing sparse representations of the two documents, the method comprising the steps of:
breaking the first and second documents into chunks of data of predefined sizes;
selecting a subset of all chunks as representative of the data in each document;
determining a set of coefficients that represent the selected chunks in each document;
combining sets of coefficients for each document into coefficient clusters for each document, a coefficient cluster containing coefficients which are similar according to a predetermined similarity metric; and
evaluating a degree of similarity between the two documents by counting coefficient clusters into which chunks from both documents fall.
2. A method as in claim 1 wherein the coefficients that represent a particular chunk are selected as Fourier transform coefficients for data values that make up the chunk.
3. A method as in claim 2 wherein the selected coefficients are the absolute values of the Fourier transform coefficients.
4. A method as in claim 2 in which the data in a chunk is mapped onto a unitary circle in a plane of complex variables before Fourier coefficients are calculated.
5. A method as in claim 1 wherein a degree of similarity is determined by calculating a correlation of coefficients of the data stored in the chunks.
6. A method as in claim 5 , in which the correlation is linear, after outliners are removed from the sets of coefficients.
7. A method as in claim 1 wherein the step of evaluating a degree of similarity is carried out in a manner to account for possible shifts in the position of similar data in the two documents.
8. A method as in claim 1 wherein the coefficient clusters are represented as a hierarchy having at least two levels, where successively lower levels of the hierarchy represent only portions of the chunks at higher levels of the hierarchy.
9. A method as in claim 8 wherein the step of evaluating proceeds first at a higher level in the hierarchy, and if a predetermined degree of similarity between coefficients of a chunk and centers of the clusters is found at the higher level, only then proceeding to compare coefficients at a lower level in the hierarchy.
10. A method as in claim 9 wherein the evaluation of coefficients of chunks to clusters at a given lower level in the hierarchy is limited to consideration of only the clusters belonging to those branches of the hierarchy which run through related higher-level clusters already determined to be similar.
11. A method as in claim 9 further comprising:
a. selecting a cluster exploration set derived from a set of coefficients located at a predetermined level of the hierarchy for the first document;
b. computing a similarity for clusters in the cluster exploration set, by comparing the clusters in the cluster exploration set against at least one chunk of the second document selected as a base element;
c. sorting the clusters so compared according to their degree of similarity to the chunk from the second element;
d. calculating a penetration similarity threshold;
e. selecting a subset of the cluster exploration set as those clusters that are similar to the base element;
f. treating the subset further as a next cluster exploration set; and
g. repeating steps b to f until the bottom of the hierarchy is reached; and
h. returning the subset generated at step f as the solution otherwise.
12. A method as in claim 11 wherein out of a subset of clusters generated at step f, a cluster which, together with its parent upper-level clusters of the hierarchy is most similar on average to a given set of coefficients is selected as a host for storing the corresponding set of coefficients.
13. A method as in claim 12 in which an average similarity of clusters at different levels of the hierarchy to the corresponding set of coefficients is given by an arithmetic average of squares of similarities of clusters at different levels with the said set of coefficients, weighted by a dimension of clusters at those levels.
14. A method as in claim 1 wherein the step of comparing further comprises: a query interpretation process for merging results of queries for multiple chunks in the hierarchy, to determine an overall degree of similarity for the two documents.
15. A method in claim 14 additionally wherein the first document is determined to be similar to a group of documents within a larger set of pre-processed documents by the further step of:
determining a number of similar chunks within the first document and all documents in the set of pre-processed documents that have been pre-processed by the method.
16. A method in claim 15 wherein documents in the set of pre-processed documents which have fewer than a predetermined number of chunks similar to the first document, are not considered to be similar.