K-means don't modify the underlying structure of your data. K-means will just provide the 'color' part of your graph.
To answer the question about why do you get a cuboid, it's because your underlying data are a cuboid. Not necessarily by construction, but that's what happen when you cap your data. As an exemple, look at the following code :
X1 = c(rnorm(...
How would you know you have to do cluster analysis before looking at your data ?
Setting aside data quality questions (which you should never do), a bare minimum of EDA will help you :
Know if it's relevant to do a clustering analysis (rarely imo)
Know if K-means is the best clustering tool (rarely imo)
Get an idea of the number of the clusters
Then you ...
There cannot be a unique answer to your question. There is a discrepancy in your question though -
I am aware that this is a classification problem on which I am working on.
Could you please help me with the right step by step guide that I should follow in order to achieve an efficient clustering at the end?
However, I am assuming that you are ...
If you are using a neural network for classification, here are a few things you can do on the data even if you don't have the labels for them. If the data points are real-valued vectors, you can normalize them by calculating the (featurewise) mean and standard deviation. You can train an autoencoder on this data (by reconstructing the original input), and ...
Look into anomaly detection algorithms. Scikit-learn has a variety of built-in ones in its outlier detection module. Finding 30 most similar points among 500 is the same as finding 470 outliers. Most of these algorithms have a parameter contamination which specifies what fraction of your dataset you generally expect to classify as outliers, so manipulating ...
If you expect that all zeros is a result of error in the measuring of the features (i.e. the observations should not be all 0s but they are), then I would say: Keep all the data, but increase k (from k-means) by 1. This extra one will hopefully become the class of all these wrong observations.
If you expect that all zeros is correct (i.e. these observations ...
It's a matter of data quality so it depends how the dataset was built:
Either these instances are meaningful, i.e. it makes sense that an observation would have zeros for all the features and that it would happen that often.
Or these are the result of an error, typically the complete absence of measurement for these observations.
Naturally one wants to ...
But can I assign the cluster labels from the pca reduced data to the original data ? would it be a right approach ? I guess not.
Yes, that is totally the right approach. Principal components are just the linear combinations of your original features that explain the most variance, so you can definitely use them for clustering. Moreover, since you only kept ...
If you want to visualise the data after K-Means, the better approach would be to reduce the dimensionality to two or three dimensions and visualise using a matplotlib 2D or 3D plot.
You might also try pair plots but I don't think It would be much helpful from clustering stand point.
It depends on the case that you are going to apply clustering. If your underlying distributions are multivariate gaussians, Mahalanobis distance might be useful. In most cases k-means is combined with Euclidean distance. However, there are cases where Euclidean distance is not useful e.g. text clustering as cosine similarity seems to be the appropriate ...
Just a few observations from my side:
I think you should clarify your goal for yourself. Writing a program isn't a goal in itself. I'm missing the top level picture.
The second part: 'The goal is to have the program automatically tag similar sounds with the correct label by finding related sounds using machine learning.' is more like a goal, but where is ...
It doesnt feel right cause there are convergence problems. See here
It has tendency to fail. With kmeans and euclidian distance you have some really nice mathematical properties and you can gurantee convergence.
As far as i see, you have your temporal columns involved. They are sequential variables and create such curves in visualization.
Try to exclude them from your data and visualize again. If you still the curves, update the post with new plot and we discuss further.
It doesn't require any special method. The algorithm of choice depends on your data if for instance Euclidean distance works for your data or not.
Generally, you can try Kmeans or other methods on your X or PCAs; but Hierarchical Clustering may be a good choice for visualizing the clusters for high dimensional data.
Please check here if you can read/write ...
There are different clustering algorithms. Without knowing every one well, I would assume it may vary.
One very popular clustering algorithm is k-means and this one usually needs scaling since
"K-means clustering is "isotropic" in all directions of space and
therefore tends to produce more or less round (rather than elongated)
clusters. In this ...
I am not sure about pixelwise distance but what I could help is on applying KMeans on this picture. Let's say I give you this picture (I cannot get your original image so I'll just use mine).
Implementing KMeans on image is actually quite straightforward. What you might want to pay attention to is to the size of the image since big image like I what I am ...
PCA and truncate SVD does not differ much, since they are based on
the same theory that the eigenvectors with the less eigenvalue are
discarded. As mentioned here the difference:
TruncatedSVD is very similar to PCA, but differs in that it works on
sample matrices directly instead of their covariance matrices. When
the columnwise (per-feature) means ...
Given that the data is labeled, just perform supervised approaches, they will almost always beat unsupervised.
Intuition why thats the case is because we dont have target function in unsupervised approach. In other words function that discriminates classes given our data set. I like to think that in unsupervised learning this function is identity function ...
I would try DBSCAN algorithm first: fairly easy to tune (with, in particular, a notion of distance as you requested), and does not need to know the number of clusters.
There are a few other algorithms that can help you decide the number of clusters: Bayesian Gaussian Mixtures (see sklearn implementation) for instance, but it requires a bit more knowledge ...
You could use K-means clustering as well here with euclidian distance measure..
Why I am suggesting euclidian distance because you have all numeric data, if it was mixed then gover distance was better pick and similarly you could pick correct distance measure based on requirements.
Here you can get the optimal number of clusters by nbclust function in R.
I'd suggest looking at hierarchical clustering:
It's simple so you could implement and tune your own version
It lets you decide at which level you want to stop grouping elements together, so you could have a maximum distance.
Be careful however that this approach can sometimes lead to unexpected/non-intuitive clusters.
With regards to the end of your question:
So the work team A is doing to cluster the instances, the tree model is is also doing per se - because segmentation is embedded in tree models.
Does this explanation make sense?
Yes, I believe this is a reasonable summary. I wouldn't say the segmentation is "embedded" in the models but a necessary step in how these ...
Not very familiar with k-medoids, but i guess it's something like k-means, right? If so, the most time-consuming part of the entire model is updating the medoids. We randomly select initial start and update the center of mass to have better cluster results.
I suggest you to pickle final_medoids. When you have new data, compute the pca, pass it to kmedoids ...
There's dedicated pypi package for incremental/online learning. It's called Creme and here's there repo. It contains KMeans implementation. Though under the hood, there's no incremental stuff going on, as you need all data in a pass (read more about Lloyd's algoritm here).
you can have a look at these suggestions
Clustering categorical data
i havent tried clustering on pure categorical dataset yet however have tried on text data where at the end you end up creating a sparse matrix and have had success there with hierarchical clustering using Wards' method
Since they are categorical variables, I would cluster them using the k-medoids clustering method. Before applying this method, one-hot encode all the predictors.
See a tutorial here:
Sklearn has an implementation:
For image clustering, I don't suggest you to start with clustering techniques immediately. First, run some dimensionality reduction model.
The most powerful dimensionality reduction technique is represented by Autoencoders. If you are familiar with Deep learning, use Autoencoders to generate compressed, dense representations of the images. Alternatively, ...
If you DO NOT have hundreds of features, then consider k-Nearest Neighbour (kNN) based outlier detection.
For a window of w unit times
calculate the median distances for the features (power,temp,..etc)
For each time stamp, calculate the kNN distance
Then you can select top 1% data points with highest deviations in kNN distances compared to the median kNN ...
You can, as an example, create a binned field for the measure. The value range can be specified in the tooltip. I used a single color based continuous palette since (population) total is a continuous field.
Based on your comment, I tested with a fixed dimension based binned field for the continuous measure. At least in my example file, the values ...
Yes its called correlation clustering.
Even though correlation can cause problems with many clustering algorithms by giving extra weight on these attributes, it would be best to drop highly correlated variables for example with PCA
However, there exist correlation clustering algorithms that are meant to process data containing multiple correlations, and ...
It would be possible with an adapted semi-supervised K-Means, also known as K-Medoids.
The tricky part with K-Means is that you do not know the centroids. However, you could hot start by assuming that some of your data points are centroids. Then, when figuring the new centroid at each iteration, instead of figuring out the "imaginary" central ...
Yes it is possible however not all algorithms support this. For example, k-means will not be able to do this, because k-means use centroid which is an "imaginary" point on the space, hence inferring distance from this point to another point on the dataset is not possible without knowing the location of every datapoints. On the other hand, DBScan is able to ...
Levenshtein distance (and its cousing Jaro, Hemming etc...)
Levenshtein distance for measuring the difference between two sequences between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word (your case one set of characters) into the other.
There are a couple implementations, for ...
First, please note that spectral clustering is very sensitive to the affinity kernel. With the standard RBF kernel, my experience is that spectral clustering often isolates outliers (in the spectral space), leaving clusters with numerous observations which can be separated by great distances. This is a major difference with direct k-means: there is no notion ...
Because assignment to microclusters is distance-based, and distances do not work in high-dimensional data anymore. Most likely one mucrocluster will become most central by chance and collect all the samples.