# clustering

Clustering is a data-analysis technique that groups observations or variables into clusters based on similarity, using methods such as hierarchical clustering and k-means.

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## Carnival of Mathematics #233

DevFeed: [Carnival of Mathematics #233](<https://devfeed.tech/articles/carnival-of-mathematics-233-40458.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2022/11/01/carnival-of-mathematics-233/>)

Published: 2024-11-01T07:00:00Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [math](<https://devfeed.tech/topics/math.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [Regular expression](<https://devfeed.tech/topics/regular-expression.md>), [Python](<https://devfeed.tech/topics/python.md>)

Tags: [carnival](<https://devfeed.tech/tags/carnival.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [fibonacci](<https://devfeed.tech/tags/fibonacci.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [python](<https://devfeed.tech/tags/python.md>), [regex](<https://devfeed.tech/tags/regex.md>)

### AI overview

A roundup of mathematics news and media from October 2022, including a newly reported largest known prime, mathematical research, discussions of political geography and clustering, and videos about voting paradoxes, polylinks, and prime-recognizing regular expressions.

### Source excerpt

Welcome to the 233rd Carnival of Mathematics! Who can forget 233, the 6th Fibonacci prime? Hey, not all numbers are interesting. Don't ask me about the smallest positive uninteresting number. You can't make it interesting with your feeble mind tricks! Anyway, on to the fun. Provers and Shakers The big discovery this month was a new largest known prime number, $2^{136279841} - 1$, as reported by the Great Internet Mersenne Prime Search.

## Semantically Compress Text to Save On LLM Costs

DevFeed: [Semantically Compress Text to Save On LLM Costs](<https://devfeed.tech/articles/semantically-compress-text-to-save-on-llm-costs-38726.md>)

Original publisher: [Read original article](<https://blog.developer.bazaarvoice.com/2024/10/28/semantically-compress-text-to-save-on-llm-costs/>)

Author: Lou Kratz

Published: 2024-10-28T13:34:46Z

Content type: article

Language: en

Sources: [Bazaarvoice](<https://devfeed.tech/sources/bazaarvoice.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [bazaarvoice](<https://devfeed.tech/tags/bazaarvoice.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [compression](<https://devfeed.tech/tags/compression.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [llm](<https://devfeed.tech/tags/llm.md>), [uncategorized](<https://devfeed.tech/tags/uncategorized.md>)

### AI overview

Bazaarvoice describes a multi-pass hierarchical clustering approach for semantically compressing large collections of user reviews so its AI Review Summaries feature can operate within LLM context-window limits and reduce token-related costs. The article says the technique allows adjustment of detail loss and helped make the feature financially feasible.

### Source excerpt

Introduction Large language models are fantastic tools for unstructured text, but what if your text doesn't fit in the context window? Bazaarvoice faced exactly this challenge when building our AI Review Summaries feature: millions of user reviews simply won't fit into the context window of even newer LLMs and, even if they did, it would [...]

## Fully Homomorphic Encryption in Production Systems

DevFeed: [Fully Homomorphic Encryption in Production Systems](<https://devfeed.tech/articles/fully-homomorphic-encryption-in-production-systems-40498.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/fhe-in-production/>)

Published: 2024-07-31T07:00:00Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [homomorphic encryption](<https://devfeed.tech/topics/homomorphic-encryption.md>), [FHE](<https://devfeed.tech/topics/fhe.md>), [Encryption](<https://devfeed.tech/topics/encryption.md>), [Cryptography](<https://devfeed.tech/topics/cryptography.md>), [Edge](<https://devfeed.tech/topics/edge.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [iOS](<https://devfeed.tech/topics/ios.md>), [Differential Privacy](<https://devfeed.tech/topics/differential-privacy.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [Library](<https://devfeed.tech/topics/library.md>)

Tags: [apple](<https://devfeed.tech/tags/apple.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [cryptography](<https://devfeed.tech/tags/cryptography.md>), [differential-privacy](<https://devfeed.tech/tags/differential-privacy.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [fhe](<https://devfeed.tech/tags/fhe.md>), [homomorphic-encryption](<https://devfeed.tech/tags/homomorphic-encryption.md>), [ios](<https://devfeed.tech/tags/ios.md>), [library](<https://devfeed.tech/tags/library.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [microsoft-edge](<https://devfeed.tech/tags/microsoft-edge.md>), [programming](<https://devfeed.tech/tags/programming.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

A living document catalogs production systems known to use fully or somewhat homomorphic encryption. It describes deployments and techniques involving Microsoft Edge password checking, Apple's Live Caller ID Lookup, and Apple's private image search, while distinguishing FHE from SHE.

### Source excerpt

In this living document, I will list all production systems I'm aware of that use fully homomorphic encryption (FHE). For background on FHE, see my overview of the field. If you have any information about production FHE systems not in this list, or corrections to information in this list, please send me an email with sufficient detail allow the claim to be publicly verified. For all production deployments, I will distinguish between cases where the deployed system does "fully" homomorphic encryption (with bootstrapping), aka FHE, and "somewhat" homomorphic encryption, aka SHE (avoiding bootstrapping).

## Advent of Kotlin Solutions

DevFeed: [Advent of Kotlin Solutions](<https://devfeed.tech/articles/advent-of-kotlin-solutions-39189.md>)

Original publisher: [Read original article](<https://kt.academy/article/advent-2021-solutions>)

Published: 2022-01-02T00:00:00Z

Content type: tutorial

Language: en

Sources: [Kt. Academy](<https://devfeed.tech/sources/kt-academy.md>)

Topics: [Kotlin](<https://devfeed.tech/topics/kotlin.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [JSON](<https://devfeed.tech/topics/json.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [json](<https://devfeed.tech/tags/json.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [pattern-matching](<https://devfeed.tech/tags/pattern-matching.md>), [recursion](<https://devfeed.tech/tags/recursion.md>), [sealed-class](<https://devfeed.tech/tags/sealed-class.md>), [workshop-learning-programming](<https://devfeed.tech/tags/workshop-learning-programming.md>)

### AI overview

This article presents Kotlin solutions for Advent of Kotlin 2021 exercises, including JSON stringification, generating well-formed parentheses, tree algorithms, and k-means clustering. It discusses recursion, sealed classes, smart casting, and Kotlin functions used in the solutions.

### Source excerpt

Solutions to the Advent of Kotlin!

## Advent of Kotlin: Week 3

DevFeed: [Advent of Kotlin: Week 3](<https://devfeed.tech/articles/advent-of-kotlin-week-3-39192.md>)

Original publisher: [Read original article](<https://kt.academy/article/advent-2021-week3>)

Published: 2021-12-14T00:00:00Z

Content type: article

Language: en

Sources: [Kt. Academy](<https://devfeed.tech/sources/kt-academy.md>)

Topics: [clustering](<https://devfeed.tech/topics/clustering.md>), [Kotlin](<https://devfeed.tech/topics/kotlin.md>)

Tags: [clustering](<https://devfeed.tech/tags/clustering.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [workshop-learning-programming](<https://devfeed.tech/tags/workshop-learning-programming.md>)

### AI overview

This article covers k-means clustering as Week 3 of the Advent of Kotlin series.

### Source excerpt

Week 3 of the Advent of Kotlin: k-means clustering

## High availability with nats-streaming-server (clustering)

DevFeed: [High availability with nats-streaming-server (clustering)](<https://devfeed.tech/articles/high-availability-with-nats-streaming-server-clustering-37814.md>)

Original publisher: [Read original article](<https://carlosbecker.com/posts/nats-streaming-server-cluster/>)

Author: Carlos Alexandro Becker

Published: 2019-05-16T00:00:00Z

Content type: tutorial

Language: en

Sources: [Carlos Becker](<https://devfeed.tech/sources/carlos-becker.md>)

Topics: [Streaming](<https://devfeed.tech/topics/streaming.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [Availability](<https://devfeed.tech/topics/availability.md>), [Raft](<https://devfeed.tech/topics/raft.md>)

Tags: [clustering](<https://devfeed.tech/tags/clustering.md>), [fault-tolerance](<https://devfeed.tech/tags/fault-tolerance.md>), [high-availability](<https://devfeed.tech/tags/high-availability.md>), [leader-election](<https://devfeed.tech/tags/leader-election.md>), [raft](<https://devfeed.tech/tags/raft.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

### AI overview

A practical guide to configuring a high-availability nats-streaming-server cluster. It compares fault-tolerance and clustering approaches, then demonstrates clustering NATS and nats-streaming-server with three nodes, RAFT leader election, replicated writes, and peer configuration.

### Source excerpt

I wanted to set up a high available nats-streaming-server cluster, but couldn't find a "quick" guide on how to do it.

## Big Dimensions, and What You Can Do About It

DevFeed: [Big Dimensions, and What You Can Do About It](<https://devfeed.tech/articles/big-dimensions-and-what-you-can-do-about-it-40396.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2016/02/08/big-dimensions-and-what-you-can-do-about-it/>)

Published: 2016-02-08T10:00:14Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [math](<https://devfeed.tech/topics/math.md>), [clustering](<https://devfeed.tech/topics/clustering.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [math](<https://devfeed.tech/tags/math.md>), [techniques](<https://devfeed.tech/tags/techniques.md>)

### AI overview

The article uses movie-title data to illustrate the curse of dimensionality: as vector dimensions grow, geometric intuition and clustering algorithms can perform poorly. It introduces dimension reduction as a way to condense high-dimensional data while retaining important geometric properties, and discusses embeddings and their mathematical guarantees.

### Source excerpt

Data is abundant, data is big, and big is a problem. Let me start with an example. Let's say you have a list of movie titles and you want to learn their genre: romance, action, drama, etc. And maybe in this scenario IMDB doesn't exist so you can't scrape the answer. Well, the title alone is almost never enough information. One nice way to get more data is to do the following:

## k-Means Clustering and Birth Rates

DevFeed: [k-Means Clustering and Birth Rates](<https://devfeed.tech/articles/k-means-clustering-and-birth-rates-40301.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/02/04/k-means-clustering-and-birth-rates/>)

Published: 2013-02-04T17:54:20Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [clustering](<https://devfeed.tech/topics/clustering.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [heuristic-algorithm](<https://devfeed.tech/tags/heuristic-algorithm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematica](<https://devfeed.tech/tags/mathematica.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [partition](<https://devfeed.tech/tags/partition.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>)

### AI overview

This tutorial introduces the clustering problem, formalizes k-means clustering as a partitioning problem over points in a metric space, explains why finding an exact solution is difficult, and describes using a heuristic algorithm instead.

### Source excerpt

A common problem in machine learning is to take some kind of data and break it up into "clumps" that best reflect how the data is structured. A set of points which are all collectively close to each other should be in the same clump. A simple picture will clarify any vagueness in this: cluster-example Here the data consists of points in the plane. There is an obvious clumping of the data into three pieces, and we want a way to automatically determine which points are in which clumps.