k-means

Reading implementation of scikit-learn in tensroflow : http://learningtensorflow.com/lesson6/ and scikit-learn : http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html I'm struggling to decide which implementation to use. scikit-learn is installed as part of the tensorflow docker container so can use either implementation. Reason to use scikit-learn : scikit-learn contains less boiler plate than the tensorflow implementation. Reason to use tensorflow : If running on Nvidia GPU the algorithm wilk be run against in parallel , I'm not sure if scikit-learn will utilise all

I'm performing a cluster analysis on categorical data, hence using k-modes approach. My data is shaped as a preference survey: How do you like hair and eyes? The respondent can pick up an answers from a fixed (multiple choice) set of 4 possibility. I therefore get the dummies, apply k-modes, attach the clusters back to the initial df and then plot them in 2D with pca. My code looks like: import numpy as np import pandas as pd from kmodes import kmodes df_dummy = pd.get_dummies(df) #transform into numpy array x = df_dummy.reset_index().values km = kmodes.KModes(n_clusters=3, init='Huang', n

Is there a online version of the k-Means clustering algorithm? By online I mean that every data point is processed in serial, one at a time as they enter the system, hence saving computing time when used in real time. I have wrote one my self with good results, but I would really prefer to have something "standardized" to refer to, since it is to be used in my master thesis. Also, does anyone have advice for other online clustering algorithms? (lmgtfy failed ;)) Yes there is. Google failed to find it because it's more commonly known as "sequential k-means". You can find two pseudo-code

I am working on a python project where I study RNA structure evolution (represented as a string for example: "(((...)))" where the parenthesis represent basepairs). The point being is that I have an ideal structure and a population that evolves towards the ideal structure. I have implemented everything however I would like to add a feature where I can get the "number of buckets" ie the k most representative structures in the population at each generation. I was thinking of using the k-means algorithm but I am not sure how to use it with strings. I found scipy.cluster.vq but I don't know how to

I am reading about the difference between k-means clustering and k-medoid clustering. Supposedly there is an advantage to using the pairwise distance measure in the k-medoid algorithm, instead of the more familiar sum of squared Euclidean distance-type metric to evaluate variance that we find with k-means. And apparently this different distance metric somehow reduces noise and outliers. I have seen this claim but I have yet to see any good reasoning as to the mathematics behind this claim. What makes the pairwise distance measure commonly used in k-medoid better? More exactly, how does the

I'm trying to implement KMeans algorithm using Pyspark it gives me the above error in the last line of the while loop. it works fine outside the loop but after I created the loop it gave me this error How do I fix this ? # Find K Means of Loudacre device status locations # # Input data: file(s) with device status data (delimited by '|') # including latitude (13th field) and longitude (14th field) of device locations # (lat,lon of 0,0 indicates unknown location) # NOTE: Copy to pyspark using %paste # for a point p and an array of points, return the index in the array of the point closest to p

I just ran PCA and then K-means Clustering algorithm on my data, after running the algorithm I get 3 clusters. I am trying to figure out which clusters my input belongs to , in order to gather some qualitative attributes about the input. My input is customer ID and the variables I used for clustering were the spend patterns on certain products Below is the code I ran for K means, looking for some inputs on how to map this back to the source data to see which cluster the input belongs to : kmeans= KMeans(n_clusters=3) X_clustered=kmeans.fit_predict(x_10d) LABEL_COLOR_MAP = {0:'r', 1 : 'g' ,2 :