A search space reduction methodology for large databases: a case study
Industrial Conference on Data Mining(2007)
摘要
Given the present need for Customer Relationship and the increased growth of the size of databases, many new approaches to
large database clustering and processing have been attempted. In this work we propose a methodology based on the idea that
statistically proven search space reduction is possible in practice. Two clustering models are generated: one corresponding
to the full data set and another pertaining to the sampled data set. The resulting empirical distributions were mathematically
tested to verify a tight non-linear significant approximation.
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关键词
large databases,proven search space reduction,empirical distribution,search space reduction methodology,increased growth,large database clustering,clustering model,present need,tight non-linear significant approximation,full data,customer relationship,new approach,case study,clustering,search space,preprocessing,sampling
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