data-mining

I have an array of 13.876(13,876) values between 0 and 1. I would like to apply sklearn.cluster.KMeans to only this vector to find the different clusters in which the values are grouped. However, it seems KMeans works with a multidimensional array and not with one-dimensional ones. I guess there is a trick to make it work but I don't know how. I saw that KMeans.fit() accepts "X : array-like or sparse matrix, shape=(n_samples, n_features)" , but it wants the n_samples to be bigger than one I tried putting my array on a np.zeros() matrix and run KMeans, but then is putting all the non-null

I'm tryin to use scikit-learn to cluster text documents. On the whole, I find my way around, but I have my problems with specific issues. Most of the examples I found illustrate clustering using scikit-learn with k-means as clustering algorithm. Adopting these example with k-means to my setting works in principle. However, k-means is not suitable since I don't know the number of clusters. From what I read so far -- please correct me here if needed -- DBSCAN or MeanShift seem the be more appropriate in my case. The scikit-learn website provides examples for each cluster algorithm. The problem

By my understanding of DBSCAN, it's possible for you to specify an epsilon of, say, 100 meters and — because DBSCAN takes into account density-reachability and not direct density-reachability when finding clusters — end up with a cluster in which the maximum distance between any two points is > 100 meters. In a more extreme possibility, it seems possible that you could set epsilon of 100 meters and end up with a cluster of 1 kilometer: see [2][6] in this array of images from scikit learn for an example of when that might occur. (I'm more than willing to be told I'm a total idiot and am

In my understanding, I thought PCA can be performed only for continuous features. But while trying to understand the difference between onehot encoding and label encoding came through a post in the following link: When to use One Hot Encoding vs LabelEncoder vs DictVectorizor? It states that one hot encoding followed by PCA is a very good method, which basically means PCA is applied for categorical features. Hence confused, please suggest me on the same. I disagree with the others. While you can use PCA on binary data (e.g. one-hot encoded data) that does not mean it is a good thing, or it

As a newbie in Machine Learning, I have a set of trajectories that may be of different lengths. I wish to cluster them, because some of them are actually the same path and they just SEEM different due to the noise. In addition, not all of them are of the same lengths . So maybe although Trajectory A is not the same as Trajectory B, yet it is part of Trajectory B. I wish to present this property after the clustering as well. I have only a bit knowledge of K-means Clustering and Fuzzy N-means Clustering . How may I choose between them two? Or should I adopt other methods? Any method that takes

How can I do K-means clustering of time series data? I understand how this works when the input data is a set of points, but I don't know how to cluster a time series with 1XM, where M is the data length. In particular, I'm not sure how to update the mean of the cluster for time series data. I have a set of labelled time series, and I want to use the K-means algorithm to check whether I will get back a similar label or not. My X matrix will be N X M, where N is number of time series and M is data length as mentioned above. Does anyone know how to do this? For example, how could I modify this k