The enormous amount of hidden data in large databases has produced incredible interests in the area of data mining. Clustering is an indispensable task in data mining to cluster data into significant subsets to obtain useful information. Clustering spatial data is a significant issue that has been broadly investigated to discover hidden patterns or useful sub-groups and has several applications like satellite imagery, geographic information systems, medical image analysis, etc. The spatial clustering approach is supposed to satisfy the necessities of the application for which the investigation is carried out. In addition the same clustering approach should be very efficient in processing data along with noise and outliers, since they are inherently present in spatial data. In recent times, several commercial data mining clustering approaches have been developed and their practice is increasing enormously to realize desired objective. Researchers are attempting their best efforts to accomplish the fast and effective algorithm for the abstraction of spatial data, which are clearly discussed in the literature. Each individual clustering approach has its own merits and demerits for processing multidimensional data and consequently in spatial clustering.