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25 papers · ranked by Valyu relevance

01
AarXiv2012
Document Clustering Evaluation: Divergence from a Random Baseline
Christopher M. De Vries, Shlomo Geva, Andrew Trotman
Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result. These concepts are defined and analysed using intrinsic and…
92relevance
02
PPubMed17 Mar 2011
Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches Clustering Accuracy of Similarity Approaches
Kevin W. Boyack, David Newman, Russell J. Duhon, Richard Klavans + 7 more
'Michael Patek' 'Joseph R. Biberstine' 'Bob Schijvenaars' 'André Skupin' 'Nianli Ma' 'Katy Börner' 'Colin Allen'] Background We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of…
91relevance
03
BbioRxiv21 Aug 2018
Docclustering: An Implementation of the Novel Ps-Document Clustering Algorithm a Preprint
Jens Dörpinghaus, Sebastian Schaaf, Marc Jacobs
Document clustering is widely used in science for data retrieval and organisation. DocClustering is developed to include and use a novel algorithm called PS-Document Clustering that has been first introduced in 2017. This method combines approaches of graph theory with state of the art NLP-technologies. This new…
90relevance
04
AarXiv2014
Document clustering using graph based document representation with constraints
Muhammad Rafi, Farnaz Amin, M. Shahid Shaikh
Document clustering is an unsupervised approach in which a large collection of documents (corpus) is subdivided into smaller, meaningful, identifiable, and verifiable sub-groups (clusters). Meaningful representation of documents and implicitly identifying the patterns, on which this separation is performed, is the…
89relevance
05
PPubMed14 Jun 2018
Soft document clustering using a novel graph covering approach
Jens Dörpinghaus, Sebastian Schaaf, Marc Jacobs
Background In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. Results In this paper we present and discuss a novel graph-theoretical approach for document…
88relevance
06
AarXiv31 Dec 2013
A Survey on Optimization Approaches to Text Document Clustering
R. Jensi, Wiselin Jiji G
Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to…
88relevance
07
PPubMed4 May 2021
Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks
Suganya Selvaraj, Eunmi Choi, Gianvito Pio, Roberto Corizzo + 1 more
'Michelangelo Ceci'] Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms…
88relevance
08
PPubMed11 Apr 2021
Improving the Retrieval of Arabic Web Search Results Using Enhanced <i> k </i> -Means Clustering Algorithm
Amjad F. Alsuhaim, Aqil M. Azmi, Muhammad Hussain, Friedhelm Schwenker
'Friedhelm Schwenker'] Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The modern writing style of Arabic excludes the…
85relevance
09
PPubMed29 May 2024
Experimental study on short-text clustering using transformer-based semantic similarity measure
Khaled Abdalgader, Atheer A. Matroud, Khaled Hossin, Xiangjie Kong
Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt…
85relevance
10
AarXiv18 Dec 2024
Information-Theoretic Generative Clustering of Documents
Xin Du, Kumiko Tanaka‐Ishii
We present generative clustering (GC) for clustering a set of documents, X, by using texts Y generated by large language models (LLMs) instead of by clustering the original documents X. Because LLMs provide probability distributions, the similarity between two documents can be rigorously defined in an…
85relevance
11
PPubMed7 Sep 2021
WEClustering: word embeddings based text clustering technique for large datasets
Vivek Mehta, Seema Bawa, Jasmeet Singh
A massive amount of textual data now exists in digital repositories in the form of research articles, news articles, reviews, Wikipedia articles, and books, etc. Text clustering is a fundamental data mining technique to perform categorization, topic extraction, and information retrieval. Textual datasets, especially…
84relevance
12
AarXiv1 Dec 2014
Automated document indexing via intelligent hierarchical clustering: A novel approach
Rajendra Kumar Roul, Shubham Rohan Asthana, Sanjay K. Sahay
—With the rising quantity of textual data available in electronic format, the need to organize it become a highly challenging task. In the present paper, we explore a document organization framework that exploits an intelligent hierarchical clustering algorithm to generate an index over a set of documents. The…
84relevance
13
AarXiv18 May 2015
Parallel Streaming Signature EM-tree
Christopher M. De Vries, Lance De Vine, Shlomo Geva, Richi Nayak
The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and…
83relevance
14
PPubMed11 Jun 2020
Solving text clustering problem using a memetic differential evolution algorithm Solving TC problem using a memetic DE algorithm
Hossam M. J. Mustafa, Masri Ayob, Dheeb Albashish, Sawsan Abu-Taleb + 1 more
'Mohd Nadhir Ab Wahab'] The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms…
82relevance
15
AarXiv31 Jan 2022
Web-Based File Clustering and Indexing for Mindoro State University
Christine A. Luzon, Luisito Lolong Lacatan, Harold Y. Bangalisan, Jayvee M. Osapdin
Method – In this paper a novel approach has been introduced for search results clustering that is based on the semantics of the retrieved documents rather than the syntax of the terms in those documents. Data clustering was used to improve the information retrieval from the collection of documents. Data were processed…
81relevance
16
BbioRxiv15 Dec 2016
Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework
Sawsan Kanj, Thomas Brüls, Stéphane Gazut
We present a new algorithm to cluster high dimensional sequence data, and its application to the field of metagenomics, which aims to reconstruct individual genomes from a mixture of genomes sampled from an environ-mental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such…
77relevance
17
BbioRxiv23 Mar 2021
VICTOR: A visual analytics web application for comparing cluster sets
Evangelos Karatzas, Maria Gkonta, Joana Hotova, Fotis A. Baltoumas + 4 more
Clustering is the process of grouping together different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate…
76relevance
18
BbioRxiv25 Nov 2017
Clusterdv, a simple density-based clustering method that is robust, general and automatic
Joao C. Marques, Michael B. Orger
How to partition a data set into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data sets vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be…
74relevance
19
BbioRxiv11 Nov 2023
SillyPutty: Improved clustering by optimizing the silhouette width
Polina Bombina, Dwayne Tally, Zachary B. Abrams, Kevin R. Coombes
Unsupervised clustering is an important task in biomedical science. We developed a new clustering method, called SillyPutty, for unsupervised clustering. As test data, we generated a series of datasets using the Umpire R package. Using these datasets, we compared SillyPutty to several existing algorithms using multiple…
71relevance
20
BbioRxiv4 May 2016
Clustering RNA-seq expression data using grade of membership models
Kushal K Dey, Chiaowen Joyce Hsiao, Matthew Stephens
Grade of membership models, also known as “admixture models”, “topic models” or “Latent Dirichlet Allocation”, are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from…
71relevance
21
CchemRxiv9 Aug 2021
Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
66relevance
22
CchemRxiv21 Oct 2024
Dedenser: a Python command line tool for clustering and downsampling chemical libraries
Authors not listed
The screening of chemical libraries is an essential starting point in the drug discovery process. While some researchers desire a more thorough screening of drug targets against a narrower scope of molecules, it is not uncommon for diverse screening sets to be favored during early stages of drug discovery. However, a…
64relevance
23
CchemRxiv17 Jan 2025
Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
64relevance
24
CchemRxiv20 Nov 2025
Comparison of internal Coordinate Frameworks for molecular Dynamics Simulations
Authors not listed
This work investigates different formulations of internal Coordinates for molecular dynamics (MD) simulations. The goal is to assess their advantages and limitations. Furthermore, a method is presented that evaluates the quality of the partitioning of molecular structural data into clusters based on statistical…
64relevance
25
CchemRxiv21 Jun 2022
From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European and US chemical agencies have listed approximately 800k chemicals where knowledge on potential risks to human health and the environment are lacking. Filling these data gaps experimentally is impossible so in-silico approaches and prediction are essential. Many existing models are however limited by…
63relevance

Results for document clustering

Document Clustering Evaluation: Divergence from a Random Baseline

Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches Clustering Accuracy of Similarity Approaches

Docclustering: An Implementation of the Novel Ps-Document Clustering Algorithm a Preprint

Document clustering using graph based document representation with constraints

Soft document clustering using a novel graph covering approach

A Survey on Optimization Approaches to Text Document Clustering

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Improving the Retrieval of Arabic Web Search Results Using Enhanced <i> k </i> -Means Clustering Algorithm

Experimental study on short-text clustering using transformer-based semantic similarity measure

Information-Theoretic Generative Clustering of Documents

WEClustering: word embeddings based text clustering technique for large datasets

Automated document indexing via intelligent hierarchical clustering: A novel approach

Parallel Streaming Signature EM-tree

Solving text clustering problem using a memetic differential evolution algorithm Solving TC problem using a memetic DE algorithm

Web-Based File Clustering and Indexing for Mindoro State University

Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework

VICTOR: A visual analytics web application for comparing cluster sets

Clusterdv, a simple density-based clustering method that is robust, general and automatic

SillyPutty: Improved clustering by optimizing the silhouette width

Clustering RNA-seq expression data using grade of membership models

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning

Dedenser: a Python command line tool for clustering and downsampling chemical libraries

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data

Comparison of internal Coordinate Frameworks for molecular Dynamics Simulations

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

Document Clustering Evaluation: Divergence from a Random Baseline

Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches Clustering Accuracy of Similarity Approaches

Docclustering: An Implementation of the Novel Ps-Document Clustering Algorithm a Preprint

Document clustering using graph based document representation with constraints

Soft document clustering using a novel graph covering approach

A Survey on Optimization Approaches to Text Document Clustering

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Improving the Retrieval of Arabic Web Search Results Using Enhanced <i> k </i> -Means Clustering Algorithm

Experimental study on short-text clustering using transformer-based semantic similarity measure

Information-Theoretic Generative Clustering of Documents

WEClustering: word embeddings based text clustering technique for large datasets

Automated document indexing via intelligent hierarchical clustering: A novel approach

Parallel Streaming Signature EM-tree

Solving text clustering problem using a memetic differential evolution algorithm Solving TC problem using a memetic DE algorithm

Web-Based File Clustering and Indexing for Mindoro State University

Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework

VICTOR: A visual analytics web application for comparing cluster sets

Clusterdv, a simple density-based clustering method that is robust, general and automatic

SillyPutty: Improved clustering by optimizing the silhouette width

Clustering RNA-seq expression data using grade of membership models

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning

Dedenser: a Python command line tool for clustering and downsampling chemical libraries

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data

Comparison of internal Coordinate Frameworks for molecular Dynamics Simulations

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization