Informatica

Analysing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, current approaches for data analysis are no longer efficient. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time: the more data points are in the dataset, the bigger the share of time needed for distance calculations.

The estimation of intrinsic dimensionality of high-dimensional data still remains a challenging issue. Various approaches to interpret and estimate the intrinsic dimensionality are developed. Referring to the following two classifications of estimators of the intrinsic dimensionality – local/global estimators and projection techniques/geometric approaches – we focus on the fractal-based methods that are assigned to the global estimators and geometric approaches. The computational aspects of estimating the intrinsic dimensionality of high-dimensional data are the core issue in this paper. The advantages and disadvantages of the fractal-based methods are disclosed and applications of these methods are presented briefly.