A new shifting grid clustering algorithm - W.M. Ma, Eden; Chow, Tommy W.S. (Pattern recognition, 2004)
A new density- and grid-based type clustering algorithm using the concept of shifting grid is proposed. The proposed algorithm is a non-parametric type, which does not require users inputting parameters. It divides each dimension of the data space into certain intervals to form a grid structure in the data space. Based on the concept of sliding window, shifting of the whole grid structure is introduced to obtain a more descriptive density profile. As a result, we are able to enhance the accuracy of the results. Compared with many conventional algorithms, this algorithm is computational efficient because it clusters data in a way of cell rather than in points.