Curse of Dimensionality in Paradoxical High Dimensional Clinical Datasets A Survey

Abstract

Data storage and retrieval is one among the challenging process in the field of computation. The storage and retrieval of multi-dimensional unstructured conflict data are needs the conversion of structure process. The storage has the major impact on access and computation time. A significant analysis of very large data sets involves different types of datasets as paradoxical high dimensional data. The ideal case assumptions are that data are collected in equal length intervals and while comparing the length are not valid for many real data sets especially clinical data sets. In addition the datasets are different from each other, the data are paradoxical and varies by each medical data. In this paper, the concept of hierarchical clustering with dendrogram structure is used to represent the paradoxical high dimensional clinical datasets. These large clusters of high dimensional datasets are of different dimensions and they may produce much noise and mask the real data to be diverse. There is a survey of clustering techniques used in paradoxical high dimensional clinical datasets and which will be highlighted by the dendrogram representation and also to reduces the dimensions of different clusters.