Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
What is the types of constraints in multidimensional gradient analysis?
The curse of dimensionality and the need for understandable results pose serious challenges for finding an efficient and scalable solution to the cubegrade problem. It can be confined but interesting version of the cubegrade problem, called constrained multidimensional gradient analysis. It can reduces the search space and derives interesting results.
There are the following types of constraints which are as follows −
Significance constraint − This provide that it can test only the cells that have specific “statistical significance” in the data, including containing at least a defined number of base cells or at least a specific total sales. In the data cube context, this constraint facilitates as the iceberg condition, which prunes a large number of trivial cells from the interpretation set.
Probe constraint − This choose a subset of cells (known as probe cells) from some possible cells as beginning points for examination. Because the cubegrade issues required to compare each cell in the cube with multiple cells that are such as specializations, generalizations, or mutations of the given cell, it derives group of same cell characteristics related to significant changes in measure in a data cube.
Given three cells, a, b, and c, if a is a description of b, then it can said that it is a descendant of b, in which method, b is a generalization or ancestor of a. Cell c is a mutation of a if the two have identical values in all but one dimension, where the dimension for which they vary cannot have a value of “*”.
Cells a and c are treated siblings. Even when treating just iceberg cubes, a multiple pairs can be generated. Probe constraints enable the user to define a subset of cells that are of interest for the analysis service. In this method, the study is targeted only on these cells and their relationships with equivalent ancestors, descendants, and siblings.
Gradient constraint − This specifies the user’s range of interest on the gradient (measure change). A user is generally interested in only specific method of changes between the cells (sectors) under comparison.
For example, it can be interested in only those cells whose average profit increases by more than 40% compared to that of the probe cells. Such changes can be defined as a threshold in the structure of either a ratio or a difference among specific measure values of the cells under comparison. A cell that taking the change from the probe cell is defined as a gradient cell.
117 Views
