K-means clustering is a type of unsupervised machine learning algorithm that is often used in text analytics to group similar documents or texts together into clusters.
The goal of k-means clustering is to identify patterns and trends within a dataset by dividing the data into a specified number of clusters based on similarity.
To use k-means clustering for text analytics, you first need to pre-process the text data in order to extract features that can be used to measure similarity.
This typically involves tokenizing the text data (i.e. breaking it down into individual words or phrases) and creating a numerical representation of the data, such as a term-frequency matrix.
Once the text data has been pre-processed, you can use the k-means algorithm to cluster the data based on similarity. The algorithm works by selecting a set of initial “centroids” (representative points) for each cluster, and then iteratively updating the centroids based on the data points that are assigned to each cluster. The algorithm continues to update the centroids until the clusters stabilize, at which point the process is complete.
One of the key advantages of k-means clustering for text analytics is that it is relatively fast and easy to implement, and it can handle large datasets. However, it can be sensitive to the initial selection of centroids, and it may not always produce the most optimal clusters.
Overall, k-means clustering is a widely used and effective tool for text analytics, and can be used to identify patterns and trends within large volumes of text data.