1.聚類問題與自組織映射

In clustering problems, you want a neural network to group data by similarity. ?For example: market segmentation done by grouping people according to their buying patterns; data mining（數據挖掘） can be done by partitioning data into related subsets; or bioinformatic analysis such as grouping genes with related expression patterns.?

A self-organizing map (selforgmap) consists of a competitive layer which can classify a dataset of vectors with any number of dimensions into as many classes as the layer has neurons. The neurons are arranged in a 2D topology, which allows the layer to form a representation of the distribution and a two-dimensional approximation of the topology of the dataset.

2. 聚類后的性能評價

原始輸入的數據 2-dimension













The default topology of the SOM is hexagonal. This figure shows the neuron locations in the topology, and indicates how many of the training data are associated
with each of the neurons (cluster centers). The topology is a 10-by-10 grid, so there are 100 neurons. The maximum number of hits associated with any neuron is 31.Thus, there are 31 input vectors in that cluster.














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