The word chosen by the documentation is a bit confusing. It says “Opposite of the value of X on the K-means objective.” It means negative of the K-means objective. K-Means Objective The objective in the K-means is to reduce the sum of squares of the distances of points from their respective cluster centroids. It has … Read more
BTW, the Fuzzy-C-Means (FCM) clustering algorithm is also known as Soft K-Means. The objective functions are virtually identical, the only difference being the introduction of a vector which expresses the percentage of belonging of a given point to each of the clusters. This vector is submitted to a “stiffness” exponent aimed at giving more importance … Read more
You have many samples of 1 feature, so you can reshape the array to (13,876, 1) using numpy’s reshape: from sklearn.cluster import KMeans import numpy as np x = np.random.random(13876) km = KMeans() km.fit(x.reshape(-1,1)) # -1 will be calculated to be 13876 here
1. K-medoid is more flexible First of all, you can use k-medoids with any similarity measure. K-means however, may fail to converge – it really must only be used with distances that are consistent with the mean. So e.g. Absolute Pearson Correlation must not be used with k-means, but it works well with k-medoids. 2. … Read more
Assuming all the values in the dataframe are numeric, # Convert DataFrame to matrix mat = dataset.values # Using sklearn km = sklearn.cluster.KMeans(n_clusters=5) km.fit(mat) # Get cluster assignment labels labels = km.labels_ # Format results as a DataFrame results = pandas.DataFrame([dataset.index,labels]).T Alternatively, you could try KMeans++ for Pandas.
The distortion, as far as Kmeans is concerned, is used as a stopping criterion (if the change between two iterations is less than some threshold, we assume convergence) If you want to calculate it from a set of points and the centroids, you can do the following (the code is in MATLAB using pdist2 function, … Read more
Interesting question. Thank you for bringing this paper to my attention – K-Means++: The Advantages of Careful Seeding In simple terms, cluster centers are initially chosen at random from the set of input observation vectors, where the probability of choosing vector x is high if x is not near any previously chosen centers. Here is … Read more
One approach is cross-validation. In essence, you pick a subset of your data and cluster it into k clusters, and you ask how well it clusters, compared with the rest of the data: Are you assigning data points to the same cluster memberships, or are they falling into different clusters? If the memberships are roughly … Read more