CLIQUE is a density-based and grid-based subspace clustering algorithm. So let’s first take a look at what is a grid and density-based clustering technique.
- Grid-Based Clustering Technique: In Grid-Based Methods, the space of instance is divided into a grid structure. Clustering techniques are then applied using the Cells of the grid, instead of individual data points, as the base units.
- Density-Based Clustering Technique: In Density-Based Methods, A cluster is a maximal set of connected dense units in a subspace.
CLIQUE Algorithm:
CLIQUE Algorithm uses density and grid-based technique i.e subspace clustering algorithm and finds out the cluster by taking density threshold and a number of grids as input parameters. It is specially designed to handle datasets with a large number of dimensions.CLIQUE Algorithm is very scalable with respect to the value of the records, and a number of dimensions in the dataset because it is grid-based and uses the Apriori Property effectively. APRIORI APPROACH ?.
Apriori Approach Stated that If an X dimensional unit is dense then all its projections in X-1 dimensional space are also dense.
This means that dense regions in a given subspace must produce dense regions when projected to a low-dimensional subspace. CLIQUE restricts its search for high-dimensional dense cells to the intersection of dense cells in the subspace because CLIQUE uses apriori properties.
Working of CLIQUE Algorithm:
The CLIQUE algorithm first divides the data space into grids. It is done by dividing each dimension into equal intervals called units. After that, it identifies dense units. A unit is dense if the data points in this are exceeding the threshold value.
Once the algorithm finds dense cells along one dimension, the algorithm tries to find dense cells along two dimensions, and it works until all dense cells along the entire dimension are found.
After finding all dense cells in all dimensions, the algorithm proceeds to find the largest set (“cluster”) of connected dense cells. Finally, the CLIQUE algorithm generates a minimal description of the cluster. Clusters are then generated from all dense subspaces using the apriori approach.
Advantage:
- CLIQUE is a subspace clustering algorithm that outperforms K-means, DBSCAN, and Farthest First in both execution time and accuracy.
- CLIQUE can find clusters of any shape and is able to find any number of clusters in any number of dimensions, where the number is not predetermined by a parameter.
- One of the simplest methods, and interpretability of results.
Disadvantage:
- The main disadvantage of CLIQUE Algorithm is that if the size of the cell is unsuitable for a set of very high values, then too much of the estimation will take place and the correct cluster will be unable to find.