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ͼƬ\effective elbow method.jpg (13775, 2018-11-20)
图片\K-means++分类.jpg (44650, 2018-11-19)
ͼƬ\meanshift.jpg (23987, 2018-11-27)
ͼƬ\meanshift3.jpg (25353, 2018-11-27)
ͼƬ\SVMdata.jpg (44020, 2018-11-19)
ͼƬ\toy_subspace_clustering.mat (3596, 2018-03-27)
ͼƬ\uneffective elbow method.jpg (15065, 2018-11-20)
图片\二维高斯分布2类.jpg (44966, 2018-11-19)
图片\二维高斯分布3类.jpg (44628, 2018-11-19)
图片\二维高斯分布4类.jpg (44754, 2018-11-19)
图片\二维高斯分布5类.jpg (44794, 2018-11-19)
图片\二维高斯分布数据.jpg (28806, 2018-11-19)
图片\初始值的影响.jpg (44355, 2018-11-15)
图片\收入不合理分类.jpg (44140, 2018-11-19)
图片\收入分类.jpg (44581, 2018-11-19)
图片\收入数据.jpg (32331, 2018-11-19)
图片\非监督学习.jpg (52629, 2018-11-19)
图片\高斯分布J.jpg (14864, 2018-11-20)
lib\DBSCAN.m (3843, 2018-03-27)
lib\Entropy_Weighting_Subspace_Kmeans.m (2089, 2018-03-27)
lib\Gaussian_Mixture.m (2685, 2018-03-27)
lib\ISODATA.m (3552, 2018-03-27)
lib\isoDataCluster.m (4640, 2018-11-17)
lib\Kmeans.m (1466, 2018-11-19)
lib\Kmeanspp.m (2713, 2018-11-19)
lib\LVQ.m (1949, 2018-03-27)
lib\meanShift.m (1550, 2018-11-21)
lib\Mean_Shift.m (3383, 2018-11-21)
lib\Subspace_Clustering.m (135, 2018-03-27)
lib\Subspace_Kmeans.m (2875, 2018-03-27)
tool\GenerateDataset.m (927, 2018-03-27)
tool\OptimalDecisionBoundary.m (229, 2018-03-27)
tool\PlotData.m (1378, 2018-03-27)
Kmeans、Kmeans++、meanshift.pptx (2084429, 2018-11-29)
MeanShift.m (1518, 2018-11-26)
seed.xlsx (21975, 2018-11-18)
新建文本文档.txt (152, 2018-11-17)
computeJ.m (519, 2018-11-19)
Kmeansdata.mat (4784, 2018-11-19)
... ...

# Clustering/Subspace Clustering Algorithms on MATLAB ## 1. Clustering Algorithms - **K-means** - **K-means++** - Generally speaking, this algorithm is similar to **K-means**; - Unlike classic K-means randomly choosing initial centroids, a better initialization procedure is integrated into **K-means++**, where observations far from existing centroids have higher probabilities of being chosen as the next centroid. - The initializeation procedure can be achieved using Fitness Proportionate Selection. - **ISODATA (Iterative Self-Organizing Data Analysis)** - To be brief, **ISODATA** introduces two additional operations: Splitting and Merging; - When the number of observations within one class is less than one pre-defined threshold, **ISODATA** merges two classes with minimum between-class distance; - When the within-class variance of one class exceeds one pre-defined threshold, **ISODATA** splits this class into two different sub-classes. - **Mean Shift** - For each point *x*, find neighbors, calculate mean vector *m*, update *x = m*, until *x == m*; - Non-parametric model, no need to specify the number of classes; - No structure priori. - **DBSCAN (Density-Based Spatial Clustering of Application with Noise)** - Starting with pre-selected core objects, DBSCAN extends each cluster based on the connectivity between data points; - DBSCAN takes noisy data into consideration, hence robust to outliers; - Choosing good parameters can be hard without prior knowledge; - **Gaussian Mixture Model (GMM)** - **LVQ (Learning Vector Quantization)** ## 2. Subspace Clustering Algorithms - **Subspace K-means** - This algorithm directly extends **K-means** to Subspace Clustering through multiplying each dimension *d_j* by one weight *m_j* (s.t. sum(*m_j*)=1, *j*=1,2,...,*p*); - It can be efficiently sovled in an Expectation-Maximization (EM) fashion. In each E-step, it updates weights, centroids using Lagrange Multiplier; - This rough algorithm suffers from the problem on its favor of using just a few dimensions when clustering sparse data; - **Entropy-Weighting Subspace K-means** - Generally speaking, this algorithm is similar to **Subspace K-means**; - In addition, it introduces one regularization item related to weight entropy into the objective function, in order to mitigate the aforementioned problem in **Subspace K-means**. - Apart from its succinctness and efficiency, it works well on a broad range of real-world datasets. - **Sparse Subspace Clustering**

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