Alternatives
Products that do what Want something better than k-means? Try BanditPAM does
Want something better than k-means? I'm happy to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available! `pip install banditpam` or `install.packages("banditpam")` and you're good to go! k-means is one of the most widely-used algorithms to cluster data. However, it has several limitations: a) it requires the use of L2 distance for efficient clustering, which also b) restricts the data you're clustering to be vectors, and c) doesn't require the means to be datapoints in the dataset. Unlike in k-means, the k-medoids problem requires cluster centers to be…
- 1AK
2018 · github.com
- 2PM
2019 · github.com
- 3PK
I built a small, self-contained K-Means implementation in pure PyTorch: https://gitlab.com/hassonofer/pt_kmeans I was working on dataset sampling and approximate nearest neighbor search, and tried several existing libraries for large-scale K-Means. I couldn't find something that was fast, simple, and would run comfortably on my own workstation without hitting memory limits. Maybe I missed an existing solution, but I ended up writing one that fit my needs. The core insight: Keep your data on CPU (where you have more RAM) and intelligently move only the necessary chunks to…
2025
- 4BI
2020 · binarysearch.io
- 5KM
2015 · github.com
- 6TV
I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
2025 · nature.com
- 7DA
Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
- 8
- 9FV
I recently found myself computing the similarity between lots of very high dimensional vectors (i.e., sentence embedding vectors from LLMs), and I wanted to try some more powerful measures of similarity/dependency than just Cosine similarity, which seems to be the default for everything nowadays because of its computational efficiency. There are many other more involved measures that can detect more subtle relationships, but the problem is that some of them are quite slow to compute, especially if you're trying to do it in Python. For my favorite measure of statistical dependency,…
2023 · github.com
- 10AG
This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.
2025 · rlafuente.com
- 11LB
2020 · github.com
- 12KA
Double-digit speed-ups over naïve K-Means Parallel by default (Rayon) Balanced & spherical variants in one crate Memory-savvy for multi-GB datasets Builder API that just feels ergonomic Docs are live, the license is Apache-2.0, and the repo is one `cargo add kentro` away. Give it a spin—curious to hear how it performs in your pipelines!
2025 · github.com
- 13IT
2021 · huggingface.co
- 14SF
I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018
- 15
- 16IS
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
- 17FA
Hey HN, I’ve built Fahmatrix, a minimal, fast Java library for working with tabular data — inspired by Python’s pandas, but designed for performance and simplicity on the JVM. After working extensively with Python’s data stack, I often ran into limitations related to speed, especially in larger or long-running data workflows. So I built Fahmatrix from scratch to offer similar APIs for manipulating CSVs, performing summary statistics, slicing rows/columns, and more — but all in Java. Features: Lightweight and dependency-free CSV/TSV import with auto-headers Series/DataFrame…
2025 · github.com
- 18GU
2018 · github.com
- 19D2
Hi! We are excited to announce the second release of Desbordante — an open-source, high-performance data profiler that is capable of discovering and validating many different patterns in data using various algorithms. Unlike existing data profilers, Desbordante focuses on discovering complex patterns in data, which are notoriously hard to extract. Since its inception in 2019, it has become the fastest open-source tool for these tasks. It also offers an array of patterns which have no alternative implementations. With this release, Desbordante now supports 17 types of patterns, such as:…
2024 · github.com
- 20IA
I am still working on the concept so feedback is welcomed! if you are interested please leave your email or let me know. I am still trying to know whether this app is worth building.
2024 · motionapps.ai
- 21UA
Hello! So, I am experimenting with new approaches to automated machine learning, where you don't need anything besides either data or prompt This is a first platform from my auto ml serie called Łukasiewicz (Jan Łukasiewicz was a logician who created Polish notation) I much appreciate your feedback and if you want to try it out, I created a -75% off promo code "HACKERNEWS" on credits, so it hopefully will cover at least some of computing expenses, but more importantly will let you play with the platform at almost no cost Thank you!
2024 · lukasiewicz.tmlc.pl
- 22PL
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
2023 · docs.giskard.ai
- 23PA
2016 · peergym.com
- 24MA
2021 · github.com
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