Hamiltonian-Based Clustering: Algorithms for Static and Dynamic Clustering in Data Mining and Image Processing

PROJECT TITLE :

Hamiltonian-Based Clustering: Algorithms for Static and Dynamic Clustering in Data Mining and Image Processing

ABSTRACT :

The giant amount of information accessible for analysis and management raises the need for defining, determining, and extracting meaningful info from the data. Hence in scientific, engineering, and economics studies, the apply of clustering knowledge arises naturally when sets of data must be divided into subgroups with the aim of presumably deducting common features for data belonging to the identical subgroup. For instance, the innovation scoreboard [1] (see Figure one) allows for the classification of the countries into four main clusters love the level of innovation defining the “leaders,” the “followers,” the “trailing,” and also the “catching up” countries. Many alternative disciplines may need or take advantage of a clustering of knowledge, from market analysis [2] to gene expression analysis [3], from biology to Image Processing [4][7]. Therefore, several clustering techniques are developed (for details see “Review of Clustering Algorithms”).

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