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Clustering analysis is a generic term for statistical procedures designed to identify groups of observations that have similar attributes. The objective of cluster analysis is to group observed data in such a way that the entities within a cluster are more similar to each other than to those in other clusters. It is one of the most widely used procedures for exploratory data analysis in many disciplines, including crystallographic statistics and web searches. Evolving clustering method is a clustering algorithm of kind of evolution, on-line and bounding by a maximum distance. It increases the number of cluster or adjusts the centers and the radius real-time dynamically as the entered sample data increasing. In any one cluster, the maximum distance between the example of cluster points and the corresponding maximum distance are less than the threshold Dthr , Dthr selection will have a direct impact on the clustering numbers. Examples of clustering process from a data stream, the whole process start clustering from an empty set. Some of the created clusters to be updated through depend on the location of the current example in the input space as well as changing the location of the cluster centers and increasing the radius of the cluster with the new examples appearance, it will no longer to be updated when its radius meet the threshold.

There are many situations where the classes themselves are initially undefined. Given a set of feature vectors sampled for some population; we would like to know if the data set consists of a number or relatively distinct subset. If it does and we can determine these subsets, we can define them to be classes. This is sometimes called class discovery. The techniques can then be used to further analyze or model the data or to clarify new data if desired. Clustering refers to the process of grouping samples so that the samples are similar within each group. The groups are called clusters.

In some applications, the main goal may be to discover the subgroups rather than to model them statistically. Fortunately, clustering techniques allow the division into subgroups to be done automatically, without any pre conceptions about what kind of groupings should be found in the community being analyzed. Cluster analysis has been applied in many fields. In image analysis, clustering can be used to find groups of pixels with similar gray levels, colors, or local textures, in order to discover the various regions in the image.

In cases where there are only two features, clusters can be found through visual inspection by looking for dense regions in a scatter plot of the data if the subgroups or classes are well separated in the feature space. If, for example, there are two bivariate normally distributed classes and their means are separated by more than two standard deviation, two distances, peaks form if there is enough data. However, distinct clusters may exist in a high-dimensional features space bay a pair of the feature axes. One general way to find candidates f本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。