Provided by: International Journal of Advanced Research in Computer Engineering & Technology
Topic: Data Management
Traditional clustering algorithms like K-means, CLARANS, BIRCH, DBSCAN etc. are not able to handle higher dimensional data because of the many issues occurred in high dimensional data e.g. \"Curse of dimensionality\", \"Irrelevant dimensions\", \"Distance problem\" etc. To cluster higher dimensional data, density and grid based, both traditional clustering algorithms combined and let to a step ahead to the traditional clustering i.e. called subspace clustering. This paper presents an important subspace clustering algorithm called CLIQUE, which deals with all the problems ensued in clustering high dimensional data.