Visualization Techniques and Algorithms

There are different types of algorithm that exists for each dissimilar type of data. The classification of algorithms depends largely on the character and function of the data. To perform its best and to express with complete application of all the visualization principles, it is required a distinct algorithm for each data type.
There are different types of data. Accordingly there are corresponding algorithms. The pack of data can be classified following its types of variables, volumes, distribution etc. All these play a major role in determining the corresponding algorithm for perfect visualization. This further provides a large spectrum of multitudinous algorithms applicable for selecting, processing, clustering and shaping abstract data.
But each algorithm is almost custom-made for each set of data. For example, the basic spring model of Eades (1984) performs best with data cluster of low dimensionality and cardinality; but on the other hand algorithms like self organizing map of Kohonen (2000) performs best with data cluster of higher cardinality. So Kohonen’s algorithm will be best to visualize a critical non linear structure with huge amount of data.
Distribution of data also plays a major role in determining its algorithm. For example, data with spherical Gaussian cluster of Bradley and Fayyad (1998) can be best visualized with algorithm of MacQueen’s K means clustering (1967). Like this there are various other algorithms like Reingold and Tilford algorithm, orthogonal combining planarization algorithm, physical simulation with analysis of principal components, hierarchical algorithm etc.
Whatever be the case, it is necessary that the algorithms work most efficiently with the data cluster. Without proper combination of data and algorithm a highly efficient visualization will not be possible. Even it has been seen that the application of hybrid algorithms can perform better with a definite set of data considering efficiency and real time visualization.
Along with visualization algorithms, there are many visualization techniques that help to get the highest efficiency of the information visualization rendering real time presentation. The popular techniques include information visualization reference model, hyperbolic tree, problem solving environment, multidimensional scaling, graph drawing, color alphabet, tree-mapping, halo etc.
With the emergence of Web 2.0, the applicability of the information visualization is increased a lot. This has created a new interactive medium with advanced flexibility of fast and compact communication. The real time visualization along with proper algorithm and techniques of visualization has created a revolution for the web world. For example, with interactive mapping of data applying proper algorithms and methods can advance the online education to a new era by discarding problematic sequential interface and textual format.