A Stochastic Approach for Clustering in Object Bases.
Jeffrey F. Naughton
Manolis M. Tsangaris
ACM SIGMOD Conference
1991
20
2
12
21
944 KB
Object clustering has long been recognized as important to the performance of object bases, but in most work to date, it is not clear exactly what is being optimized or how optimal are the solutions obtained. We give a rigorous treatment of a fundamental
Information Systems, INFORMATION STORAGE AND RETRIEVAL, Information Search and Retrieval, Clustering.
Theory of Computation, ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, Nonnumerical Algorithms and Problems, Computations on discrete structures.
Information Systems, DATABASE MANAGEMENT, Systems.
Mathematics of Computing, DISCRETE MATHEMATICS, Graph Theory, Graph algorithms.
Mathematics of Computing, PROBABILITY AND STATISTICS, Probabilistic algorithms (including Monte Carlo).
G.2.2
Mathematics of Computing, DISCRETE MATHEMATICS, Graph Theory, Graph algorithms.
H.3.3
Information Systems, INFORMATION STORAGE AND RETRIEVAL, Information Search and Retrieval, Clustering.
F.2.2
Theory of Computation, ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, Nonnumerical Algorithms and Problems, Computations on discrete structures.
G.3
Mathematics of Computing, PROBABILITY AND STATISTICS, Probabilistic algorithms (including Monte Carlo).
H.2.4
Information Systems, DATABASE MANAGEMENT, Systems.