Algorithmic Techniques for Massive Data Sets

Charikar, Moses

Princeton University

关键词: Data Analysis;    Data Base Management Massive Data Sets, Efficient Algorithms, Dimension Reduction, Clustering, Similarity Search;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Information Retrieval;    Algorithms;   

DOI  :  10.2172/881082
RP-ID  :  DOE/ER/25540
RP-ID  :  FG02-02ER25540
RP-ID  :  881082

美国|英语

来源: UNT Digital Library

【 摘 要 】

This report describes the progress made during the Early Career Principal Investigator (ECPI) project on Algorithmic Techniques for Large Data Sets. Research was carried out in the areas of dimension reduction, clustering and finding structure in data, aggregating information from different sources and designing efficient methods for similarity search for high dimensional data. A total of nine different research results were obtained and published in leading conferences and journals.