Hierarchical clustering algorithm

The standard hierarchical clustering algorithms produce a whole set of cluster solutions, namely a partitioning of the objects into k 1, n clusters. The partitions are ordered hierarchically, and there are two possible procedures ... 

Further details of agglomerative, and several other clustering strategies may be found in the book by MASSART and KAUFMAN [1983] or, along with remarks on the treatment of situations with missing values, in the monograph by MUCHA [1992]. Finally, it may be of interest that OZAWA [1983] even proposed a hierarchical cluster algorithm based on an asymmetric distance matrix. 

In this passage we demonstrate that comparable results may also be obtained when other methods of unsupervised learning, e.g. the non-hierarchical cluster algorithm CLUPOT [COOMANS and MASSART, 1981] or the procedure of the computation of the minimal spanning tree [LEBART et al., 1984], which is similar to the cluster analysis, are applied to the environmental data shown above. 

Murtagh, F. A survey ofrecent advances in hierarchical clustering algorithms. Comput. 

Mining, 32 (8), 68 (1999). Chameleon A Hierarchical Clustering Algorithm Using Dynamic Modeling. 

As stated earlier, hierarchical clustering algorithms operate on a type of tree called a dendrogram. Each leaf of the dendrogram contains one and only one element of Q and all elements have a leaf node. From these leaf nodes... 

Many hierarchical clustering algorithms are based on ultrametric partitionings of the objects. As described by Johnson (1967), an ultrametric d between clusters satisfies the usual metric conditions plus the ultrametric inequality,... 

The main step used in hierarchical clustering algorithm in analyzing gene expression data is to compare every entity with all the other entities by calculating a distance. The calculation of the distance depends on the linkage method being implemented and the method of calculation of the actual distances. There are three major... 

The traditional hierarchical and nonhierarchical (e.g., fc-means) clustering algorithms [69] have a number of drawbacks that require caution in their implementation for time series data. The hierarchical clustering algorithms assume an implicit parent-child relationship between the members of a cluster which may not be relevant for time series data. However, they can provide good initial estimates of patterns that may exist in the data set. The fc-means algorithm requires the estimate of the number of clusters (i.e., k) and its solution depends on the initial assignments as the optimization... 

Karypis G, Han J, Kumar V. CHAMELEON A hierarchical clustering algorithm using dynamic modeling. Computer 1999 32 68-75. 

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