BanditPAM: Fast k-medoids Clustering with Multi-Armed Bandits
Introduction:
Are you looking for a better alternative to the commonly used k-means algorithm? Look no further! Our state-of-the-art k-medoids algorithm, BanditPAM, is now publicly available and can be easily installed with just a simple command: pip install banditpam. The k-medoids problem, like k-means, involves partitioning a dataset into separate subsets. However, in k-medoids, the cluster centers must be actual datapoints, which allows for greater interpretability. Additionally, k-medoids is more robust to outliers and supports arbitrary distance metrics, making it suitable for clustering a wide range of data types. Our NeurIPS paper introduces BanditPAM, which drastically reduces the runtime complexity of k-medoids algorithms and offers parallelization and caching features. With our implementation, you can enjoy all the advantages of k-medoids with minimal changes to your existing code. Don’t miss out on this opportunity to improve your clustering results!
Full Article: BanditPAM: Fast k-medoids Clustering with Multi-Armed Bandits
New State-of-the-Art Clustering Algorithm, BanditPAM, Now Available for ML Practitioners
If you’re a machine learning (ML) practitioner, you’re probably familiar with the popular (k)-means problem and its common algorithms. However, there’s another clustering problem called (k)-medoids that you may not be as familiar with. The (k)-medoids problem is similar to (k)-means, where the objective is to partition a dataset into subsets based on the closeness to a single cluster center. However, in (k)-medoids, the cluster centers must be actual datapoints, which makes the clusters more interpretable.
Benefits of (k)-Medoids
There are several advantages to using (k)-medoids over (k)-means. First, by requiring the cluster centers to be actual datapoints, solutions tend to be more interpretable. For example, when clustering images from the ImageNet dataset, the mean of a (k)-means solution may be a nondescript blob, whereas the medoid of a (k)-medoids solution is an actual image.
Also, (k)-medoids supports arbitrary distance metrics, allowing for more robust clustering. It can handle “exotic” objects like strings, natural language, trees, and graphs without the need to embed them in a vector space first. This is unlike (k)-means, which typically requires the (L_2) metric for efficiency.
Furthermore, (k)-medoids can be more robust to outliers when using robust distance metrics. For example, the (L_1) metric is more robust to outliers than the (L_2) metric.
Challenges with (k)-Medoids
Despite these advantages, (k)-medoids has been less widely used than (k)-means due to its slower runtime. The best-known (k)-medoids algorithms scaled quadratically in dataset size (i.e., (O(n^2)) complexity), while the best (k)-means algorithms scaled linearly (i.e., (O(n)) complexity).
Introducing BanditPAM
In a recent paper presented at NeurIPS, a new algorithm called BanditPAM was introduced to address the runtime challenges of (k)-medoids. BanditPAM is a state-of-the-art (k)-medoids algorithm that significantly reduces the complexity from (O(n^2)) to (O(nlog n)). This complexity is almost on par with standard (k)-means algorithms.
BanditPAM is written in C++ for speed and supports parallelization and intelligent caching. It can be easily installed using the command “pip install banditpam.” Importantly, BanditPAM’s interface is compatible with the (texttt{sklearn.cluster.KMeans}) interface, making it easy to integrate into existing code with minimal changes.
How BanditPAM Works
BanditPAM builds on the Partitioning Around Medoids (PAM) algorithm, which was proposed in 1990. PAM is a greedy solution to the (k)-medoids problem and consists of two steps: the BUILD step and the SWAP step.
In the BUILD step, each of the (k) medoids is initialized greedily. This step has (O(n^2)) computational complexity since it requires computing pairwise distances between all datapoints.
The SWAP step considers all possible (medoid, non-medoid) pairs and computes the change in loss that would result from swapping the medoid assignment. This step also has (O(n^2)) time complexity.
BanditPAM’s key insight is that it doesn’t need to compute all pairwise distances for each step of the PAM algorithm. Instead, it intelligently samples a subset of distances, reducing unnecessary computation. By treating each step as a multi-armed bandit problem, BanditPAM identifies the best actions to take with a reduced complexity of (O(nlog n)).
Try BanditPAM Now
BanditPAM offers a high-performance, speed-optimized solution for the (k)-medoids problem. You can install it easily with the command “pip install banditpam” and start using it with just a few changes to your existing code. With BanditPAM, you can improve the interpretability of your clustering results and handle various distance metrics efficiently. Give it a try and see the benefits for yourself.
Summary: BanditPAM: Fast k-medoids Clustering with Multi-Armed Bandits
If you’re looking for a better alternative to the popular (k)-means algorithm in machine learning, we have a solution for you. Our state-of-the-art (k)-medoids algorithm called BanditPAM is now available for public use. You can easily install it with (texttt{pip install banditpam}). Similar to (k)-means, (k)-medoids is a clustering problem that aims to partition a dataset into separate subsets. However, (k)-medoids requires the cluster centers to be actual data points, making them more interpretable. In addition, (k)-medoids works well with various distance metrics, providing more robust clustering results. Despite these advantages, (k)-medoids hasn’t been widely used due to slow algorithms. In our NeurIPS paper, BanditPAM, we have significantly sped up the best-known (k)-medoids algorithm, reducing its complexity from (O(n^2)) to (O(nlog n)). Our implementation is written in C++ for speed and supports parallelization and intelligent caching, without any extra complexity for users. It also has a similar interface to (texttt{sklearn.cluster.KMeans}), allowing for minimal changes to existing code. If you want to learn more about the benefits and workings of the BanditPAM algorithm, check out our detailed explanation in the provided link.
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