Paper 1
Banded Pattern Mining Algorithms In Multi-Dimensional Zero-One DataAuthors: Fatimah B. Abdullahi, Frans Coenen, and Russell Martin |
AbstractA zero-one high-dimensional data set is said to be banded if all the dimensions can be reorganised such that the \non zero” entries are arranged along the leading diagonal across the dimensions. Our goal is to develop e ective algorithms that identify banded patterns in multi- dimensional zero-one data by automatically rearranging the ordering of all the dimensions. Rearranging zero-one data so as to feature \banded- ness” allows for the identi cation of hidden information and enhances the operation of many data mining algorithms (and other algorithms) that work with zero-one data. In this paper two N-Dimensional Banded Pat- tern Mining (NDBPM) algorithms are presented. The rst is an approx- imate algorithm (NDBPMAPPROX) and the second an exact algorithm (NDBPMEXACT ). Two variations of NDBPMEXACT are presented (Euclidean and Manhattan). Both algorithms are fully described together with evaluations of their operation. |