I am using Hierarchical Agglomerative Clustering in scikit-learn, to cluster texts. How can i get text labels for each clusters.
clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)
is there any parameter to get it, or we have to write our own logic for that.
Related
Recently I trained a BYOL model on a set of images to learn an embedding space where similar vectors are close by. The performance was fantastic when I performed approximate K-nearest neighbours search.
Now the next task, where I am facing a problem is to find a clustering algorithm that uncovers a set of clusters using the embedding vectors generated by the BYOL trained feature extractor [dimension of the vector is 1024 & there are 1 million vectors]. I have no information apriori about the number of classes i.e. clusters in my dataset & thus cannot use Kmeans. Is there any scalable clustering algorithm that can help me uncover such clusters. I tried to use FISHDBC but the repository does not have good documentation.
I am using the pyspark.ml.clustering library to train a K-Means clustering model. I am able to train it and obtain the cluster centres, here is the piece of code attached below. I am obtaining the centres as a list (ref to the code), my task is that I want to obtain the original data labels for each column. Any help is appreciated!
I have used the StringIndexer, OneHotEncoder, StandardScalar and VectorAssembler as feature engineering before training the model on the normalized features column.
centers = model.clusterCenters()
ClusterCenter = [x.tolist() for x in centers]
display(ClusterCenter)
I am trying to implement k-means clustering on Spark using Python and i want to specify the initial centroids instead of taking 'random' or 'k-means++'. I want to pass an RDD which contains the list of centroids. How should I do this in Pyspark.
How to extrapolate a sample of 10,000 rows to the entire population (100,000) in python. I did agglomerative clustering on the sample in python, stuck with extrapolating the result to the entire population.
There is no general rule.
For hierarchical clustering, this very much depends on your linkage, and the clustering of a different sample or the whole population may be very different. (For a starter, try a different sample and compare!)
Generalizing a clustering result to new data is usually contradicting the very assumptions made for the clustering. This is not classification, but explorative data analysis.
However, if you have found good clustering results, and you have verified them to be desirable, then you can train a classifier on the cluster labels to predict the cluster label of new data.
Can anyone help me to find a dataset have scores as attribute values and having the class labels(Ground Truth for cluster validation).I want to find the probability of each data item and inturn use it for clustering.
The preferable attribute values are scores like user survey scores(1-bad,2-satisfactory,3-good,4-very good) for each of the attributes.I am preferring score values(say 1,2,3,4) as attribute values as it is easy to calculate probability of each attribute value from these score values.
I found some datasets from UCI Repository but not all attribute values were score values.
Most (if not all) clustering algorithms are density based.
There is plenty of survey literature on clustering algorithm that you need to check. There are literary hundreds of density based algorithms, including DBSCAN, OPTICS, DENCLUE, ...
However, I have the impression you are using the term "density based" different than literature. You seem to refer to probability, not density?
Do not expect clustering to give class labels. Classes are not clusters. Classes can be inseparable, or a single class may consists of multiple clusters. The famous iris data set, for example, intuitively consists only of 2 clusters (but 3 classes).
For evaluation and all that, check existing questions and answers, please.