Current information technology development coupled to modem astronomical instrumentation is leading to accelerated acquisition of large volumes of observational data, fast pipeline processing and enormous on-line archives containing high quality images and multi-dimensional parameter values of astronomical objects. This is the case for facilities covering a wide spectral range. A few recent examples are the Sloan Digital Sky Survey (SDSS), the 2 Micron All Sky Survey (2MASS) and the NRAO VLA Sky Survey (NVSS). Each of these provide catalogues of many millions of objects. with often several tens of parameters characterizing the objects. In the near future these data volumes will increase by an order of magnitude when a new generation of instruments comes on-line. In this thesis. we proposed visual and computational paradigms to analyze and extract information out of this flood of data. To obtain such techniques problems of a twofold nature needed to be overcome: one is the huge size of the datasets and the other is their large dimensionality Density estimation approaches can be used for handling large size. There exist several techniques that can be used to obtain density profiles of the data. However. if we we want to use the outcome of such estimators in later stages of the process, they need to fulfill certain criteria. such as computational efficiency. correctness, etc. In Chapter 2 we studied the performance of four density estimation techniques: k-nearest neighbors (kNN). adaptive Gaussian kernel density estimation (DEDlCA), a special case of adaptive Epanechnikov kernel density estimation (MBE). and the Delaunay tessellation field estimator (DTFE). The adaptive kernel based methods. especially MBE, perform better than the other methods in terms of calculating the density properly. and have stronger predictive power in astronomical use cases. Moreover, the computation time of these methods is lower than other methods and they compute the density on grids which can facilitate visualization (as an image in 20 and a volume in 3D) and analysis of the data, Using the MBE method we can also achieve scalability in terms of number of data points. After the original feature space has been transformed into image space. further computation can be done in image space that has constant size. although the size of the dataset can grow very large.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.