有哪些实现半监督(约束)集群的软件包? [英] What are some packages that implement semi-supervised (constrained) clustering?

问题描述

我想对半监督(约束)集群进行一些实验，特别是将背景知识作为实例级别的成对约束(必须链接或不能链接约束)提供.我想知道是否有任何好的开源软件包可以实现半监督集群?我尝试查看PyBrain，mlpy，scikit和orange，但找不到任何受约束的聚类算法.特别是，我对约束K均值或基于约束密度的聚类算法(例如C-DBSCAN)感兴趣. 首选使用Matlab，Python，Java或C ++的程序包，但不必限于这些语言.

I want to run some experiments on semi-supervised (constrained) clustering, in particular with background knowledge provided as instance level pairwise constraints (Must-Link or Cannot-Link constraints). I would like to know if there are any good open-source packages that implement semi-supervised clustering? I tried to look at PyBrain, mlpy, scikit and orange, and I couldn't find any constrained clustering algorithms. In particular, I'm interested in constrained K-Means or constrained density based clustering algorithms (like C-DBSCAN). Packages in Matlab, Python, Java or C++ would be preferred, but need not be limited to these languages.

推荐答案

python软件包 scikit-learn 现在具有用于 Ward层次聚类(自0.15起)和聚集聚类(自0.14起)的算法，这些算法支持

The python package scikit-learn has now algorithms for Ward hierarchical clustering (since 0.15) and agglomerative clustering (since 0.14) that support connectivity constraints.

此外，我确实有一个真实的应用程序，即从单元位置识别轨道，其中每个轨道在每个时间点只能包含一个位置.

Besides, I do have a real world application, namely the identification of tracks from cell positions, where each track can only contain one position from each time point.

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