Information retrieval and text mining using distributed...

Details Information retrieval and text mining using distributed... Information retrieval and text mining using distributed...

2006-12-19

2006-12-19

Robinson, Greta (Department: 2168)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000

Reexamination Certificate

active

07152065

ABSTRACT:
The use of latent semantic indexing (LSI) for information retrieval and text mining operations is adapted to work on large heterogeneous data sets by first partitioning the data set into a number of smaller partitions having similar concept domains. A similarity graph network is generated in order to expose links between concept domains which are then exploited in determing which domains to query as well as in expanding the query vector. LSI is performed on those partitioned data sets most likely to contain information related to the user query or text mining operation. In this manner LSI can be applied to datasets that heretofore presented scalability problems. Additionally, the computation of the singular value decomposition of the term-by-document matrix can be accomplished at various distributed computers increasing the robustness of the retrieval and text mining system while decreasing search times.

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Steinbach, M., et al., “A Comparison of Document Clustering Techniques,” Technical Report 00-034, Department of Computer Science and Engineering, University of Minnesota, 2000.

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