Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
381301 | Engineering Applications of Artificial Intelligence | 2009 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Angel Kuri-Morales, Fátima Rodríguez-Erazo,