具有离散和连续属性的聚类算法？ [英] Clustering Algorithm with discrete and continuous attributes?

问题描述

有人知道在离散和连续属性上执行聚类的好的算法吗？我正在研究一个确定一组相似客户并且每个客户都具有离散和连续属性的问题（请考虑客户类型，该客户产生的收入金额，地理位置等）。

Does anyone know a good algorithm for perform clustering on both discrete and continuous attributes? I am working on a problem of identifying a group of similar customers and each customer has both discrete and continuous attributes (Think type of customers, amount of revenue generated by this customer, geographic location and etc..)

传统上，像K-means或EM的算法可用于连续属性，如果我们混合使用连续和离散属性怎么办？

Traditionally algorithm like K-means or EM work for continuous attributes, what if we have a mix of continuous and discrete attributes?

推荐答案

如果我没记错的话，那么COBWEB算法可以使用离散属性。

If I remember correctly, then COBWEB algorithm could work with discrete attributes.

您还可以对离散属性执行不同的技巧为了创建有意义的距离度量标准。

And you can also do different 'tricks' to the discrete attributes in order to create meaningful distance metrics.

您可以用google搜索分类/离散属性的群集，这是第一批匹配项： ROCK：一种用于分类属性的鲁棒聚类算法。

You could google for clustering of categorical/discrete attributes, one of the first hits: ROCK: A Robust Clustering Algorithm for Categorical Attributes.

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