cluster-analysis

I want to select columns from a data frame so that the resulting continuous column-sequences are as long as possible, while the number of rows with NAs is as small as possible, because they have to be dropped afterwards. (The reason I want to do this is, that I want to run TraMineR::seqsubm() to automatically get a matrix of transition costs (by transition probability) and later run cluster::agnes() on it. TraMineR::seqsubm() doesn't like NA states and cluster::agnes() with NA states in the matrix doesn't necessarily make much sense.) For that purpose I already wrote a working function that

Is there a code/library that can calculate a Voronoi diagram for planes (parallelograms) in 3D? I checked Qhull and it seems it can only work with points, in its examples Voro++ works with different size of spheres but I couldn't find anything for polygons. In this image (sample planes in 3d) the parallelograms are 3D since they have a thickness, but in this case the thickness will be zero.! Voronoi cells are not parallelograms. You are confused here by the image you posted. Voronoi cell borders are parts of the hyperplanes that are separating the individual means. Check out this website

I am using Spectral Clustering method to cluster my data. The implementation seems to work properly. However, I have one problem - I have a set of unseen points (not present in the training set) and would like to cluster these based on the centroids derived by k-means (Step 5 in the paper). However, the k-means is computed on the k eigenvectors and therefore the centroids are low-dimensional. Does any-one knows a method that can be used to map an unseen point to a low-dimension and compute the distance between the projected point and the centroids derived by k-means in step 5. Late answer, but

I am trying to obtain document clustering using Weka. The process is a part of a larger pipeline, and I really can't afford to write out arff files. I have all the documents and the bag of words in each document as a Map<String, Multiset<String>> structure, where the keys are document names, and the Multiset<String> values are the bags of words in the documents. I have two questions, really: (1) Current approach ends up clustering terms, not documents: public final Instances buildDocumentInstances(TreeMap<String, Multiset<String>> docToTermsMap, String encoding) throws IOException { int

I have time series data of different length of series. I want to cluster based upon DTW distance but could not find ant library regarding it. sklearn give straight error while tslearn kmeans gave wrong answer. My problem is solving if I pad it with zeros but I am not sure if this is correct to pad time-series data while clustering. The suggestion about other clustering technique about time series data are welcomed. max_length = 0 for i in train_1: if(len(i)>max_length): max_length = len(i) print(max_length) train_1 = sequence.pad_sequences(train_1, maxlen=max_length) km3 = TimeSeriesKMeans(n

Using the heatmap function of made4 , I made this heatmap dendrogram from the example file: data(khan) heatplot(khan$train[1:30,], lowcol="blue", highcol="red") How can I add a panel of labels for the samples on the edges of the heatmap, like in this figure? The labels in this case are the squares that are adjacent to the heatmap first col and top row, used to denote a label for each sample so that one can see if the labels correspond with the clustering shown by the heatmap/dendrogram. In this particular plot they chose to make those labels correspond exactly to the colors of the dendrogram

I have a question about scipy's kmeans and kmeans2 . I have a set of 1700 lat-long data points. I want to spatially cluster them into 100 clusters. However, I get drastically different results when using kmeans vs kmeans2 . Can you explain why this is? My code is below. First I load my data and plot the coordinates. It all looks correct. import pandas as pd, numpy as np, matplotlib.pyplot as plt from scipy.cluster.vq import kmeans, kmeans2, whiten df = pd.read_csv('data.csv') df.head() coordinates = df.as_matrix(columns=['lon', 'lat']) plt.figure(figsize=(10, 6), dpi=100) plt.scatter