2015年1月16日 · K-means is working perfectly, it's just optimizing the wrong criterion. Even on perfect data sets, it can get stuck in a local minimum. Below is the best of 10 runs of k-means …

2013年1月29日 · If k-means would be initialized as the first setting then it would be stuck.. and that's by no means a global minimum. You can use a variant of previous example to create …

K-means is all about the analysis-of-variance paradigm. ANOVA - both uni- and multivariate - is based on the fact that the sum of squared deviations about the grand centroid is comprised of …

2015年1月2日 · Also, as all the centers are initialized randomly in k-means, it can give different results than k-means++. K-means can give different results on different runs. The k-means++ …

2014年3月12日 · (K-means might be a bad choice, too - you need something that can handle lat/lon naturally) If you have mixed numerical data, where each attribute is something entirely …

2011年4月3日 · 2) Scikit-learn clustering gives an excellent overview of k-means, mini-batch-k-means ... with code that works on scipy.sparse matrices. 3) Always check cluster sizes after k …

A naive approach to attack this problem would be to combine k-Means clustering with Levenshtein distance, but the question still remains "How to represent "means" of strings?". There is a …

2022年4月28日 · I am doing K-means using MINST dataset. However, I found difficulties in the implementation on initialization and some further steps. For the initialization, I have to first pick …

2009年10月9日 · sklearn k-means and sklearn other clustering algorithms. scipy k-means and scipy k-means2. Old answer: Scipy's clustering implementations work well, and they include a …

2012年7月2日 · This is a question about k-means clustering algorithm. I have the following points and clustering of data S1.