Nonhierarchical clustering methods

Examples of nonhierarchical clustering [22] methods include Gaussian mixture models, means, and fuzzy C means. They can be subdivided into hard and soft clustering methods. Hard classification methods such as means assign pixels to membership of only one cluster whereas soft classifications such as fuzzy C means assign degrees of fractional membership in each cluster. 

Figure 5.16 Clustering selection methods nonhierarchical approaches.

Cluster-Based Methods. Clustering methods have a long history of application in chemical information (60). Any set of descriptors can be used in the clustering, but most typically some form of structural fingerprint is used in conjunction with a similarity measure such as the Tanimoto coefficient (see Section 2.1.4.1). The methods fall into two broad classes, hierarchical and nonhierarchical. 

Willett, P. Winterman, V. Bawden, D. Implementation of Nonhierarchic Clustering Methods in Chemical Information Systems Selection of Compounds for Biological Testing and the Clustering of Substructure and Search Output . J. Chem. Inf. Comput. Sci. 1986,26, 109-118. 

P Willett, V Wmterman, D Bawden. Implementation of nonhierarchic cluster analysis methods m chemical information systems Selection of compounds for biological testing and clustering of substiaictures search output. I Chem Inf Comput Sci 26 109-118, 1986. 

There are two main types of clustering techniques hierarchical and nonhierarchical. Hierarchical cluster analysis may follow either an agglomerative or a divisive scheme agglomerative techniques start with as many clusters as objects and, by means of repeated similarity-based fusion steps, they reach a final situation with a unique cluster containing all of the objects. Divisive methods follow exactly the opposite procedure they start from an all-inclusive cluster and then perform a number of consecutive partitions until there is a bijective correspondence between clusters and objects (see Fig. 2.12). In both cases, the number of clusters is defined by the similarity level selected. 

The aim of classification by nonhierarchical clustering is to classify the objects under consideration into a certain number of preliminary intended clusters. The clusters are formed simultaneously by partitioning methods, which allow the objects to be rearranged between the clusters. The main disadvantage of nonhierarchical clustering is the absence of a graphical output. 

Implementation of Nonhierarchic Cluster-Analysis Methods in Chemical Information Systems Selection of Compounds for Biological Testing and Clustering of Substructure Search Output. 

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