Clustering by Rui Xu

By Rui Xu

This is often the 1st publication to take a very accomplished examine clustering. It starts off with an advent to cluster research and is going directly to discover: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential info clustering; large-scale information clustering; facts visualization and high-dimensional information clustering; and cluster validation. The authors suppose no earlier history in clustering and their beneficiant inclusion of examples and references help in making the subject material understandable for readers of various degrees and backgrounds.

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A circle is obtained when L2 is used, while squares are generated for L1 and L∞ metrics. For L1, the vertices of the square are on the axes, and for L∞, the square’s sides are parallel to the axes. D ( x i , x j ) = max xil − x jl . 21) Note that the invariance to translations and rotations is no longer valid for other cases of the Minkowski metric (Jain and Dubes, 1988). Now let us consider a set of points satisfying Dp(xi, x0) = 1, where x0 represents the origin. Here, we use the subscript p to represent that the distance is measured with the Minkowski metric.

1996). For both spotted cDNA and oligonucleotide microarrays, the DNA clones are either robotically spotted or ejected from a nozzle onto the support. The former is called contact printing and the latter is known as inkjet printing. Inkjet printing technology can also be used to eject the four different types of nucleotides and build the desired sequences using one nucleotide at a time. The GeneChip uses photolithography technology from the semiconductor industry to manufacture oligonucleotide microarrays.

BIRCH can achieve a computational complexity of O(N). 2 CURE and ROCK As a midway between the single linkage (or complete linkage) method and the centroid linkage method (or k-means, introduced in Chapter 4), which use either all data points or one data point as the representatives for the generated cluster, CURE uses a set of well-scattered points to represent each cluster 42 HIERARCHICAL CLUSTERING Start Build a CF tree Decrease the tree size if necessary Perform agglomerative hierarchical clustering Refine the clusters End Fig.

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