cluster-analysis

I have a graph of N vertices where each vertex represents a place. Also I have vectors, one per user, each one of N coefficients where the coefficient's value is the duration in seconds spent at the corresponding place or 0 if that place was not visited. E.g. for the graph: the vector: v1 = {100, 50, 0 30, 0} would mean that we spent: 100secs at vertex 1 50secs at vertex 2 and 30secs at vertex 4 (vertices 3 & 5 where not visited, thus the 0s). I want to run a k-means clustering and I've chosen cosine_distance = 1 - cosine_similarity as the metric for the distances, where the formula for cosine

I have a data table ("norm") containing numeric - at least to what I can see - normalized values of the following form: When I am executing k <- kmeans(norm,center=3) I am receving the following error: Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1) Can you help me? Thank you! kmeans cannot handle data that has NA values. The mean and variance are then no longer well defined, and you don't know anymore which center is closest. Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1) This error occurs also due to non numeric values present in the table. all of

Here is my simple example of dealing with data clustering in 3 attribute(x,y,value). each sample represent its location(x,y) and its belonging variable. My code was post here: x = np.arange(100,200,1) y = np.arange(100,200,1) value = np.random.random(100*100) xx,yy = np.meshgrid(x,y) xx = xx.reshape(100*100) yy = yy.reshape(100*100) j = np.dstack((xx,yy,value))[0,:,:] fig = plt.figure(figsize =(12,4)) ax1 = plt.subplot(121) xi,yi = np.meshgrid(x,y) va = value.reshape(100,100) pc = plt.pcolormesh(xi,yi,va,cmap = plt.cm.Spectral) plt.colorbar(pc) ax2 = plt.subplot(122) y_pred = KMeans(n_clusters

I wonder if Triangle inequality is necessary for the distance measure used in kmeans. k-means is designed for Euclidean distance, which happens to satisfy triangle inequality. Using other distance functions is risky, as it may stop converging . The reason however is not the triangle inequality, but the mean might not minimize the distance function . (The arithmetic mean minimizes the sum-of-squares, not arbitrary distances!) There are faster methods for k-means that exploit the triangle inequality to avoid recomputations. But if you stick to classic MacQueen or Lloyd k-means, then you do not

my topic is similarity and clustering of (a bunch of) text(s). In a nutshell: I want to cluster collected texts together and they should appear in meaningful clusters at the end. To do this, my approach up to now is as follows, my problem is in the clustering. The current software is written in php. 1) Similarity: I treat every document as a "bag-of-words" and convert words into vectors. I use filtering (only "real" words) tokenization (split sentences into words) stemming (reduce words to their base form; Porter's stemmer) pruning (cut of words with too high & low frequency) as methods for