BibSonomy bookmarks for /tag/clustering

[2603.09229] Flash-KMeans: Fast and Memory-Efficient Exact K-Means

analyst — 2026-03-17T20:20:43+01:00

Can a Rock song have a Jazz audience? Relationship between folksonomy and collaborative filtering in music recommender systems

lban — 2026-03-16T11:52:06+01:00

https://www.researchgate.net/publication/228421874_Can_a_Rock_song_have_a_Jazz_audience_Relationship_between_folksonomy_and_collaborative_filtering_in_music_recommender_systems

FINEX: A Fast Index for Exact & Flexible Density-Based Clustering

scch — 2026-01-19T08:11:02+01:00

https://dl.acm.org/doi/abs/10.1145/3588925

scikit-learn-contrib/hdbscan: A high performance implementation of HDBSCAN clustering.

bshanks — 2025-04-28T23:55:46+02:00

https://github.com/scikit-learn-contrib/hdbscan

Everlaw Launches AI-based Clustering to Open a New World of Ediscovery Insights to Legal Teams

becker — 2022-07-07T22:06:57+02:00

/PRNewswire/ -- Everlaw, the cloud-native investigation and litigation platform, unveiled its Clustering software feature today, delivering an AI breakthrough...

Linked Document Embedding for Classification | Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

parismic — 2020-10-23T11:03:33+02:00

https://dl.acm.org/doi/abs/10.1145/2983323.2983755

OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers | Bioinformatics | Oxford Academic

marcsaric — 2020-10-21T00:09:57+02:00

https://academic.oup.com/bioinformatics/article/35/22/4788/5522012

Contextual Topic Identification - Insight Data Science

ghagerer — 2020-08-26T14:22:25+02:00

Identifying meaningful topics for sparse Steam reviews -- by Steve Shao

visualizing RSS clustering

selm — 2020-07-25T23:13:31+02:00

http://monkey.org/~jose/graphing/rss-cluster/

Gaussian Mixture Model clustering: how to select the number of components (clusters)

ghagerer — 2020-06-30T11:11:53+02:00

You want to discern how many clusters we have (or, if you prefer, how many gaussians components generated the data), and you don’t have information about the “ground truth”. A real case, where data do not have the nicety of behaving good as the simulated ones.

arvkevi/kneed: Knee point detection in Python

becker — 2020-03-16T19:21:45+01:00

https://github.com/arvkevi/kneed

Latent Semantic Analysis & Sentiment Classification with Python

ghagerer — 2019-09-02T16:49:37+02:00

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them. LSA itself is an unsupervised way of uncovering synonyms in a collection of documents. To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way.

Topic Modeling with LSA, PLSA, LDA & lda2Vec

ghagerer — 2019-09-02T16:37:16+02:00

In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling. In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.

[ Paper Summary ] Unsupervised Deep Embedding for Clustering Analysis

becker — 2019-05-06T22:55:26+02:00

https://towardsdatascience.com/paper-summary-unsupervised-deep-embedding-for-clustering-analysis-e269aed77c6b

What is the relation between k-means clustering and PCA? - Cross Validated

becker — 2019-05-06T21:57:25+02:00

https://stats.stackexchange.com/questions/183236/what-is-the-relation-between-k-means-clustering-and-pca

Installation — leidenalg 0.7.0.post1+4.g3bf0ecb documentation

becker — 2019-02-16T00:16:42+01:00

https://leidenalg.readthedocs.io/en/latest/install.html

python - change certain squares in a seaborn heatmap - Stack Overflow

becker — 2018-12-14T09:43:03+01:00

https://stackoverflow.com/questions/31707033/change-certain-squares-in-a-seaborn-heatmap

[1811.08916] On the small scale clustering of quasars: constraints from the MassiveBlack II simulation

miki — 2018-11-26T10:34:04+01:00

https://arxiv.org/abs/1811.08916

[1811.00556] The clustering of typical Ly$\alpha$ emitters from $z \sim 2.5 - 6$: host halo masses depend on Ly$\alpha$ and UV luminosities

miki — 2018-11-05T10:45:23+01:00

https://arxiv.org/abs/1811.00556

50096 - Coloring the clusters in a dendrogram

jkd — 2018-10-06T07:06:36+02:00

The %CLUSTERGROUPS macro creates a custom template that combines a dendrogram and a blockplot to highlight each of the specified number of clusters with a different color. The %CLUSTERGROUPS macro enhances dendrograms produced in SAS by adding color to highlight the clusters. You specify the number of clusters desired as input to the macro.