Clustering Methods in Bioinformatics
Density-based clustering
Learning objectives:
Introduction
Density-based clustering is a method of clustering data points in a dataset based on the density of data points in the region. This method is beneficial for identifying clusters of data points that are compact and well-separated from other clusters.
This tutorial will discuss the basics of density-based clustering and how it can be applied to real-world data. We will also discuss some of this method's key advantages and limitations and compare it to other clustering techniques. After finishing this tutorial, you will understand how density-based clustering works and how to apply it to your data.
Some standard density-based clustering algorithms include DBSCAN and HDBSCAN. These algorithms work by identifying clusters of high density and expanding the cluster until the density falls below a certain threshold. The method allows them to identify clusters of arbitrary shapes and sizes, making them well-suited for data that does not have well-defined, spherical clusters.
One of the key advantages of density-based clustering is its ability to handle noise and outlier data points. Since these points are not part of any dense region, they are not included in the identified clusters. The ability to handle noise can benefit real-world data, which often contains outliers and noise.
However, density-based clustering also has some limitations. It requires a good understanding of the density threshold to identify clusters correctly, and it can be sensitive to the choice of density threshold.
It also requires a good understanding of the underlying data distribution, as the algorithm may identify multiple clusters with only one underlying cluster if the data is not well-understood.
Overall, density-based clustering is a powerful tool for identifying clusters in data, but it is essential to understand its limitations and how to apply it properly to your data.
The Algorithms
DBSCAN Algorithm
HDBSCAN Algorithm
Comparison of DBSCAN and HDBSCAN
Using density-based clustering on your data
To begin using density-based clustering on your data, you must choose an appropriate density-based clustering algorithm. As mentioned earlier, some standard density-based clustering algorithms include DBSCAN and HDBSCAN.
Once you have chosen an algorithm, you will need to select appropriate parameter values. For DBSCAN, this includes the minimum number of points required to form a cluster (min_samples) and the maximum distance between two points to be considered in the same cluster (eps).
For HDBSCAN, you will need to choose the minimum cluster size (min_cluster_size) and the minimum samples for a cluster to be considered a "core sample" (min_samples).
It is crucial to choose appropriate parameter values for your data, as these will have a significant impact on the identified clusters. One common approach is cross-validation to tune the parameters and select the best-performing set of values.
Once you have chosen your algorithm and set the appropriate parameters, you can apply density-based clustering to your data by calling the fit method on your chosen algorithm and passing in the data as a parameter. The algorithm will then identify the clusters in the data and assign each data point to a cluster.
You can then visualize the clusters by plotting the data points and coloring them by their assigned cluster. Visualization can give you a sense of how well the density-based clustering algorithm is performing and whether the identified clusters are meaningful.
It is also a good idea to evaluate the performance of the density-based clustering algorithm using a cluster evaluation metric, such as the adjusted Rand index or the silhouette score. Such metrics give you a quantitative measure of how well the algorithm performs and help you compare it to other clustering algorithms.
That is it! You now have a basic understanding of how to use density-based clustering to identify clusters in your data. With practice and careful parameter selection, you can use this powerful tool to gain insight into your data and uncover hidden patterns.