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Clustering AND Machine learning: techniques

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20 Documents

LlamaIndex 🦙 sur X : "An emerging technique to better represent your data for RAG/LLM applications is to only chunk the data, but also hierarchically cluster and index it..."

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2024-03-30 About

[2210.16637] Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

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2022-11-25 About

Nils Reimers sur Twitter : "Creating intent classes for chatbots is challenging This tutorial shows how to use sentence-transformers to find potentially overlapping intent classes and how to improve your data annotation work." / Twitter

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2022-02-19 About

Nils Reimers sur Twitter : "how to use the fast clustering algorithm from sentence-transformers..."

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2022-02-19 About

sentence-transformers/fast_clustering.py at master · UKPLab/sentence-transformers

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2022-02-18 About

Application of Self-Organizing Maps in Text Clustering: A Review | IntechOpen (2012)

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2021-10-17 About

[2103.12953] Supporting Clustering with Contrastive Learning

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2021-05-20 About

A Self-Training Approach for Short Text Clustering - (Hadifar 2019)

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2021-05-20 About

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 About

[Jaeyoung2018] Patent Document Clustering with Deep Embeddings

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2019-02-23 About

[1511.06335] Unsupervised Deep Embedding for Clustering Analysis

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2019-02-19 About

How to do Unsupervised Clustering with Keras – Chengwei Zhang – Medium

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2018-06-09 About

Convolutional Neural Networks on Graphs

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2018-05-05 About

A Tutorial on Spectral Clustering

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Spectral clustering

2018-05-05 About

In Depth: Gaussian Mixture Models | Python Data Science Handbook

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The non-probabilistic nature of [k-means](/tag/k_means_clustering) and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations. We take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind k-means, but can also be a powerful tool for estimation beyond simple clustering.

A Gaussian mixture model (GMM) attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset. In the simplest case, GMMs can be used for finding clusters in the same manner as k-means.

But because GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments (in Scikit-Learn, using predict_proba)

> Though GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for density estimation. That is to say, the result of a GMM fit to some data is technically not a clustering model, but a generative probabilistic model describing the distribution of the data.

-> a natural means of determining the optimal number of components for a given dataset

2018-03-16 About

[1602.06797] Semi-supervised Clustering for Short Text via Deep Representation Learning

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2017-11-07 About

[1701.00185] Self-Taught Convolutional Neural Networks for Short Text Clustering

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2017-11-04 About

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (slides)

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2017-07-11 About

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (2004) (paper)

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2017-07-11 About

Document Representation and Dimension Reduction for Text Clustering

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2017-06-24 About