ABSTRACT
There is no single algorithm that can fully satisfy all the above requirements. It is important to understand the characteristics of each algorithm so that the proper algorithm can be selected for the clustering problem at hand. Recently, there are several new clustering techniques offering useful advances, possibly even complete solutions. During the past decades, clustering analysis has been used to deal with practical problems in many applications, as summed up by Han [30, 29]. Biology. Biologists have spent many years creating a taxonomy (hierarchical classifi cation) of all living things: kingdom, class, order, family, genus and species. More recently, biologists have applied clustering to analyze the large amounts of genetic information that are now available. For example, clustering has been used to fi nd groups of genes that have similar functions from high dimensional genes data.
