Can k-means be used for text clustering?

K-means clustering is a type of unsupervised learning method, which is used when we don’t have labeled data as in our case, we have unlabeled data (means, without defined categories or groups). The goal of this algorithm is to find groups in the data, whereas the no. of groups is represented by the variable K.

Which clustering algorithm is best for text data?

for clustering text vectors you can use hierarchical clustering algorithms such as HDBSCAN which also considers the density. in HDBSCAN you don’t need to assign the number of clusters as in k-means and it’s more robust mostly in noisy data.

Can we get different results for different runs of K-means clustering A?

K-Means clustering algorithm instead converses on local minima which might also correspond to the global minima in some cases but not always. However, note that it’s possible to receive same clustering results from K-means by setting the same seed value for each run.

What are the limitations of the K-means clustering algorithm?

The most important limitations of Simple k-means are: The user has to specify k (the number of clusters) in the beginning. k-means can only handle numerical data. k-means assumes that we deal with spherical clusters and that each cluster has roughly equal numbers of observations.

Can k-means be used for categorization of text data?

K-means is classical algorithm for data clustering in text mining, but it is seldom used for feature selection. We use k-means method to capture several cluster centroids for each class, and then choose the high frequency words in centroids as the text features for categorization.

Can you cluster text data?

Text clustering is the task of grouping a set of unlabelled texts in such a way that texts in the same cluster are more similar to each other than to those in other clusters. Text clustering algorithms process text and determine if natural clusters (groups) exist in the data.

Why applying k-means clustering to the same dataset twice may give different results?

It sounds like an initialization issue. K-means generally needs some initial cluster assignment or set of cluster centers to start with. The two differing results might hence likely be two local minima of the function (minimal distances to class means) that k-means optimizes.

How can the limitations of K means clustering potentially affect a given analysis?

It requires to specify the number of clusters (k) in advance. It can not handle noisy data and outliers. It is not suitable to identify clusters with non-convex shapes.