k-means

问题 I am trying to cluster a grayscale image using Kmeans. First, I have a question: Is Kmeans the best way to cluster a Mat or are there newer more efficient approaches? Second, when I try this: Mat degrees = imread("an image" , IMREAD_GRAYSCALE); const unsigned int singleLineSize = degrees.rows * degrees.cols; Mat data = degrees.reshape(1, singleLineSize); data.convertTo(data, CV_32F); std::vector<int> labels; cv::Mat1f colors; cv::kmeans(data, 3, labels, cv::TermCriteria(cv::TermCriteria::EPS

问题 I have a set of data with 50 features (c1, c2, c3 ...), with over 80k rows. Each row contains normalised numerical values (ranging 0-1). It is actually a normalised dummy variable, whereby some rows have only few features, 3-4 (i.e. 0 is assigned if there is no value). Most rows have about 10-20 features. I used KMeans to cluster the data, always resulting in a cluster with a large number of members. Upon analysis, I noticed that rows with fewer than 4 features tends to get clustered together

问题 In Python sklearn KMeans (see documentation), I was wondering what happens internally when passing an ndarray of shape (n, n_features) to the init parameter, When n<n_clusters Does it drop the given centroids and just starts a kmeans++ initialization which is the default choice for the init parameter ? (PDF paper kmeans++) (How does Kmeans++ work) Does it consider the given centroids and fill accordingly the remaining centroids using kmeans++ ? Does it consider the given centroids and fill

问题 This Question is more Theoretical, and not specifically trying to problem-solve. I recently was introduced to the K-Means Clustering algorithm, and unsupervised machine learning algorithm, and I was intrigued by the though that one some sets of data, even if completely random, the average centroids drawn could keep changing through each iteration. Example: What I am trying to show here, is, imagine if the program flipped between iteration 6, to iteration 9, and kept doing this forever. I have

问题 I am performing a binary classification of a partially labeled dataset. I have a reliable estimate of its 1's, but not of its 0's. From sklearn KMeans documentation: init : {‘k-means++’, ‘random’ or an ndarray} Method for initialization, defaults to ‘k-means++’: If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. I would like to pass an ndarray, but I only have 1 reliable centroid, not 2. Is there a way to maximize the entropy between the K

问题 I am performing a binary classification of a partially labeled dataset. I have a reliable estimate of its 1's, but not of its 0's. From sklearn KMeans documentation: init : {‘k-means++’, ‘random’ or an ndarray} Method for initialization, defaults to ‘k-means++’: If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. I would like to pass an ndarray, but I only have 1 reliable centroid, not 2. Is there a way to maximize the entropy between the K

问题 I am currently trying to view the created centroids(cluster centers) for each iteration of KMeans that is determined from each iteration of n_init. As of now I am able to view the final results but I would like to see these at each iteration so I am able to report the differences of KMeans when using init='random' and preset cluster centers at each iteration. The following is a brief example of what I currently have \ #Creating model for Kmeans Model=[] Model=KMeans(n_clusters=5,max_iter=10,n

问题 I am currently trying to view the created centroids(cluster centers) for each iteration of KMeans that is determined from each iteration of n_init. As of now I am able to view the final results but I would like to see these at each iteration so I am able to report the differences of KMeans when using init='random' and preset cluster centers at each iteration. The following is a brief example of what I currently have \ #Creating model for Kmeans Model=[] Model=KMeans(n_clusters=5,max_iter=10,n

问题 I am currently trying to view the created centroids(cluster centers) for each iteration of KMeans that is determined from each iteration of n_init. As of now I am able to view the final results but I would like to see these at each iteration so I am able to report the differences of KMeans when using init='random' and preset cluster centers at each iteration. The following is a brief example of what I currently have \ #Creating model for Kmeans Model=[] Model=KMeans(n_clusters=5,max_iter=10,n

问题 I am currently trying to view the created centroids(cluster centers) for each iteration of KMeans that is determined from each iteration of n_init. As of now I am able to view the final results but I would like to see these at each iteration so I am able to report the differences of KMeans when using init='random' and preset cluster centers at each iteration. The following is a brief example of what I currently have \ #Creating model for Kmeans Model=[] Model=KMeans(n_clusters=5,max_iter=10,n