Mining Top-K Multidimensional Gradients

11 years 7 days ago
Mining Top-K Multidimensional Gradients
Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing gradient data analysis one is able to identify trends, outliers and answering “what-if” questions over large databases. Gradient queries were first introduced by Imielinski et al [1] as the cubegrade problem. The main idea is to detect interesting changes in a multidimensional space (MDS). Thus, changes in a set of measures (aggregates) are associated with changes in sector characteristics (dimensions). MDS contains a huge number of cells which poses great challenge for mining gradient cells on a useful time. Dong et al [2] have proposed gradient constraints to smooth the computational costs involved in such queries. Even by using such constraints on large databases, the number of interesting cases to evaluate is still large. In this work, we are interested to explore best cases (Top-K cells) of interestin...
Ronnie Alves, Orlando Belo, Joel Ribeiro
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Authors Ronnie Alves, Orlando Belo, Joel Ribeiro
Comments (0)