模糊均值聚类算法。

基于时空聚类算法的轨迹停驻点识别研究.pdf

无监督学习 五个不同聚类算法之间的比较 有关报告和结果的信息，请阅读“无监督学习的中期工作.pdf”文件

基本上是copy的压缩包里的文档。原程序只能处理1维的，改成了可以处理任意维的；改成了文件读取数据；修正了内存泄漏的问题。

智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真代码

为了进一步提高双聚类结果的性能，提出了一种基于变分贝叶斯的半监督双聚类算法。首先，在双聚类过程中引入了行和列的辅助信息，并提出了相应的联合分布概率模型；然后基于变分贝叶斯学习方法对联合概率分布中的参数进行估计；最后，通过合成数据集和真实的基因表达式数据集对提出的算法性能进行评估。实验表明，提出的算法在进行双聚类分析时，其归一化互信息量明显优于相关的双聚类算法。

机器学习 代码分享机器学习 代码分享机器学习 代码分享

Abstract—Clustering face images according to their latent identity has two important applications: (i) grouping a collection of face images when no external labels are associated with images, and (ii) indexing for efficient large scale face retrieval. The clustering problem is composed of two key parts: representation and similarity metric for face images, and choice of the partition algorithm. We first propose a representation based on ResNet, which has been shown to perform very well in image classification problems. Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarities between face images. This allows a dynamic selection of number of clusters and retains pairwise similarities between faces. ConPaC formulates the clustering problem as a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to find an approximate solution for maximizing the posterior probability of the adjacency matrix. Experimental results on two benchmark face datasets (LFW and IJB-B) show that ConPaC outperforms well known clustering algorithms such as k-means, spectral clustering and approximate Rank-order. Additionally, our algorithm can naturally incorporate pairwise constraints to work in a semi-supervised way that leads to improved clustering performance. We also propose an k-NN variant of ConPaC, which has a linear time complexity given a k-NN graph, suitable for large datasets. Index Terms—face clustering, face representation, Conditional Random Fields, pairwise constraints, semi-supervised clustering.

高光谱遥感图像模糊c均值聚类算法的matlab实现

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