data-mining

I am trying to find a code that actually works to find the most frequently used two and three word phrases in R text mining package (maybe there is another package for it that I do not know). I have been trying to use the tokenizer, but seem to have no luck. If you worked on a similar situation in the past, could you post a code that is tested and actually works? Thank you so much! You can pass in a custom tokenizing function to tm 's DocumentTermMatrix function, so if you have package tau installed it's fairly straightforward. library(tm); library(tau); tokenize_ngrams <- function(x, n=3)

Cosine Similarity article on Wikipedia Can you show the vectors here (in a list or something) and then do the math, and let us see how it works? I'm a beginner. Here are two very short texts to compare: Julie loves me more than Linda loves me Jane likes me more than Julie loves me We want to know how similar these texts are, purely in terms of word counts (and ignoring word order). We begin by making a list of the words from both texts: me Julie loves Linda than more likes Jane Now we count the number of times each of these words appears in each text: me 2 2 Jane 0 1 Julie 1 1 Linda 1 0 likes

UPDATED: In the end, the solution I opted to use for clustering my large dataset was one suggested by Anony-Mousse below. That is, using ELKI's DBSCAN implimentation to do my clustering rather than scikit-learn's. It can be run from the command line and with proper indexing, performs this task within a few hours. Use the GUI and small sample datasets to work out the options you want to use and then go to town. Worth looking into. Anywho, read on for a description of my original problem and some interesting discussion. I have a dataset with ~2.5 million samples, each with 35 features (floating

I have an image of size as RGB uint8(576,720,3) where I want to classify each pixel to a set of colors. I have transformed using rgb2lab from RGB to LAB space, and then removed the L layer so it is now a double(576,720,2) consisting of AB. Now, I want to classify this to some colors that I have trained on another image, and calculated their respective AB-representations as: Cluster 1: -17.7903 -13.1170 Cluster 2: -30.1957 40.3520 Cluster 3: -4.4608 47.2543 Cluster 4: 46.3738 36.5225 Cluster 5: 43.3134 -17.6443 Cluster 6: -0.9003 1.4042 Cluster 7: 7.3884 11.5584 Now, in order to classify/label

Possible Duplicate: Cluster one-dimensional data optimally? So let's say I have an array like this: [1,1,2,3,10,11,13,67,71] Is there a convenient way to partition the array into something like this? [[1,1,2,3],[10,11,13],[67,71]] I looked through similar questions yet most people suggested using k-means to cluster points, like scipy , which is quite confusing to use for a beginner like me. Also I think that k-means is more suitable for two or more dimensional clustering right? Are there any ways to partition an array of N numbers to many partitions/clustering depending on the numbers? Some