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11. Stop Using Elbow Method in K-means Clustering, Instead, Use this! | by Anmol Tomar | Towards Data Science

    Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering

    2023-02-13 Tags: elbow, silhouette, optimization, k for k-means, clustering, machine learning, python by klotz
12. Stanislas Morbieu – Accuracy: from classification to clustering evaluation

    2019-06-06 Tags: clustering, classification, accuracy, machine learning by klotz
13. Spectral Clustering for beginners - Towards Data Science

    2019-07-15 Tags: clustering, spectral clustering by klotz
14. Scikit Learn - Clustering Methods

    Comparing Clustering Algorithms
    Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.
    
    Sr.No Algorithm Name Parameters Scalability Metric Used
    1 K-Means No. of clusters Very large n_samples The distance between points.
    2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance
    3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points.
    4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance
    5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points.
    6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance
    7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points.
    8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.

    2021-10-29 Tags: machine learning, clustering, scikit-learn, python, tutorial, cheatsheet by klotz
15. Powerful machine-learning technique enables biologists to analyze enormous data sets

    2019-03-18 Tags: umap, clustering, dimensionality, deep learning, machine learning, biology by klotz
16. OpenAI Embeddings API Benchmarking

    2022-01-31 Tags: openai, api, word embedding, embedding, nlp, use cases, similarity, clustering, topic modellinf by klotz
17. NLP Lecture 7 - Lexical Semantics and Word Embeddings

    Word embeddings are suitable for use with neural network language models (as will be discussed later); they can also be used to enhance conventional (MEMM, CRF) models. The best ways to incorporate embeddings into such feature-based language models are still being explored. The simplest approach involves the direct use of the vector components as features (Turian et al 2010, Word Representations: A Simple and General Method for Semi-Supervised Learning, ACL 2010; Nguyen and Grishman, ACL 2014). Less direct approaches include building clusters from the embeddings and then using the clusters as features, or selecting prototypical examples of each type and then using similarity to these prototypes (based on embedding similarity) as features. Early results on NE tagging indicate a small advantage for the indirect methods (Guo et al., Revisiting embedding features for simple semi-supervised learning, EMNLP 2014). Models based on word embeddings are producing the best performance on named entity recognition (A. Passos et al, Lexicon Infused Phrase Embeddings for Named Entity Resolution, CoNLL 2014) and are effective for chunking (Turian et al ACL 2010).

    2016-04-01 Tags: nlp, word embedding, word2vec, wordnet, clustering by klotz
18. Mastering Customer Segmentation with LLM

    Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques

    2023-09-28 Tags: llm, segmentation, kmeans, k prototype, tsne, pca, sentence, embedding, clustering, exploratory data analysis by klotz
19. Mapping the tech world with t-SNE - Towards Data Science

    2020-01-16 Tags: t-sne, deep learning, nlp, text, clustering, dimensionality reduction, medium by klotz
20. Machine Learning for Humans, Part 3: Unsupervised Learning

    2018-09-19 Tags: clustering, dimensionality reduction, k-means clustering, hierarchical clustering, pca, svd, tutorial, medium by klotz

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