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# What is Grid Based Methods?

The grid-based clustering methods use a multi-resolution grid data structure. It quantizes the object areas into a finite number of cells that form a grid structure on which all of the operations for clustering are implemented. The benefit of the method is its quick processing time, which is generally independent of the number of data objects, still dependent on only the multiple cells in each dimension in the quantized space.

An instance of the grid-based approach involves STING, which explores statistical data stored in the grid cells, WaveCluster, which clusters objects using a wavelet transform approach, and CLIQUE, which defines a grid-and density-based approach for clustering in high-dimensional data space.

STING is a grid-based multiresolution clustering method in which the spatial area is divided into rectangular cells. There are generally several levels of such rectangular cells corresponding to multiple levels of resolution, and these cells form a hierarchical mechanism each cell at a high level is separation to form several cells at the next lower level. Statistical data regarding the attributes in each grid cell (including the mean, maximum, and minimum values) is precomputed and stored.

Statistical parameters of higher-level cells can simply be calculated from the parameters of the lower-level cells. These parameters contain the following: the attribute-independent parameter, count, and the attribute-dependent parameters, mean, stdev (standard deviation), min (minimum), max (maximum); and the type of distribution that the attribute value in the cell follows, including normal, uniform, exponential, or none (if the distribution is anonymous).

When the records are loaded into the database, the parameters count, mean, stdev,
min, and a max of the bottom-level cells are computed directly from the records. The
value of distribution can be assigned by the user if the distribution type is known
beforehand or obtained by hypothesis tests including the χ^{2} test.

The kind of distribution of a higher-level cell that can be computed depends on the majority of distribution types of its corresponding lower-level cells in conjunction with a threshold filtering procedure. If the distributions of the lower-level cells disagree with each other and decline the threshold test, the distribution type of the high-level cell is set to none.

The statistical parameters can be used in top-down, grid-based approaches as follows. First, a layer within the hierarchical architecture is decided from which the query-answering procedure is to start. This layer generally includes a small number of cells. For every cell in the current layer, it can compute the confidence interval (or estimated range of probability) reflecting the cell’s relevancy to the given query.

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