# When labelled data is not available, what are some common unsupervised learning algorithms for pattern recognition that can be used?

In pattern recognition systems, when no labeled data is available, what are some common unsupervised learning algorithms for pattern recognition, that can be used?

In other pattern recognition problems, the training data consists of a set of input vectors $$x$$ without any corresponding target values. The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.