CLIQUE Clustering Algorithm: A Grid-Based Density Approach for High-Dimensional Data Spaces

The CLIQUE clustering algorithm employs a grid-based density approach to achieve clustering in high-dimensional data spaces. Unlike other clustering algorithms, it does not require pre-specification of the number of clusters; instead, it automatically identifies clusters based on data characteristics and density distribution. This makes it particularly useful for handling diverse and large-scale datasets. From an implementation perspective, CLIQUE operates in two key phases: grid partitioning and dense unit identification. First, it divides the data space into a multi-dimensional grid structure using equal-sized intervals. Then, it identifies dense units by counting data points within each grid cell and applying a density threshold. Connected dense units are merged to form clusters through a depth-first or breadth-first search approach. For example, in medical research, CLIQUE can be applied to cluster patient gene expression data, enabling identification of distinct patient subtypes without prior knowledge of subtype counts. The algorithm's core functions involve efficient subspace searching and density calculation, often implemented using bitmap representations for memory optimization. In summary, the CLIQUE clustering algorithm serves as a powerful tool for exploratory data analysis in high-dimensional contexts, providing automated cluster discovery while handling dimensionality challenges through its grid-density hybrid methodology.