<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Clustering on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/clustering/</link><description>Recent content in Clustering on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/clustering/index.xml" rel="self" type="application/rss+xml"/><item><title>Speaker Diarization</title><link>https://terms-en.ai-term-hub.com/en/terms/speaker_diarization/</link><pubDate>Sat, 18 Jul 2026 10:16:18 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/speaker_diarization/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Speaker Diarization is the task of partitioning an audio stream into homogeneous segments according to the identity of the speaker. It combines speaker change detection with speaker clustering to label segments with unique speaker IDs. This technology is essential for making multi-party conversations understandable in transcripts, often referred to as the &amp;lsquo;who said what&amp;rsquo; problem.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>The process of determining &amp;lsquo;who spoke when&amp;rsquo; in an audio recording.&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>Speaker clustering&lt;/li>
&lt;li>Identity labeling&lt;/li>
&lt;li>Who-said-what&lt;/li>
&lt;li>Audio segmentation&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>Automatic meeting minutes generation&lt;/li>
&lt;li>Interview transcription&lt;/li>
&lt;li>Broadcast media analysis&lt;/li>
&lt;/ul>
&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/speaker_change_detection/">speaker_change_detection&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/speech_to_text/">speech_to_text&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/voice_printing/">voice_printing&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/audio_analysis/">audio_analysis&lt;/a>&lt;/li>
&lt;/ul></description></item><item><title>Hierarchical Risk Parity</title><link>https://terms-en.ai-term-hub.com/en/terms/hierarchical_risk_parity/</link><pubDate>Sat, 18 Jul 2026 10:01:08 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/hierarchical_risk_parity/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Hierarchical Risk Parity (HRP) is a portfolio construction method that addresses the limitations of traditional mean-variance optimization by incorporating correlation structures. It utilizes hierarchical clustering algorithms to group assets based on their similarity, then allocates capital recursively through the dendrogram structure. This approach ensures diversification by treating clusters as distinct units, reducing sensitivity to estimation errors in covariance matrices and providing more robust out-of-sample performance compared to classical methods.&lt;/p></description></item><item><title>EM algorithm and GMM model</title><link>https://terms-en.ai-term-hub.com/en/terms/em_algorithm_and_gmm_model/</link><pubDate>Sat, 18 Jul 2026 09:56:25 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/em_algorithm_and_gmm_model/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>This term refers to the synergistic relationship between the Expectation-Maximization (EM) algorithm and Gaussian Mixture Models (GMM). A GMM assumes that all data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. Since the specific component generating each point is unknown (latent variable), the EM algorithm is employed to estimate these parameters iteratively. The E-step computes the expected value of the latent variables, while the M-step updates the parameters to maximize the likelihood. This combination is fundamental in clustering and density estimation tasks where data exhibits multimodal distributions.&lt;/p></description></item></channel></rss>