Comparison of Algorithms for Document Clustering

Gupta, Mamta; Rajavat, Anand

doi:10.1109/cicn.2014.123

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…We explored different nonlinear approaches including manifold learning, kernel PCA (KPCA), isometric mapping (IsoMap), locally linear embedding (LLE), multidimensional scaling (MDS), and uniform manifold approximation and projection (UMAP) on the word embeddings. 53), Ward linkage in Shehata (54), and BIRCH in Gupta and Rajavat (55). With the exception of UMAP and spherical k-means, all the dimensionality reduction and clustering algorithms mentioned above have been implemented in Python using the Scikit-learn library (56).…”

Section: Dimensionality Reduction and Clusteringmentioning

confidence: 99%

Social determinants of health derived from people with opioid use disorder: Improving data collection, integration and use with cross-domain collaboration and reproducible, data-centric, notebook-style workflows

Markatou

Kennedy²,

Brachmann³

et al. 2023

Front. Med.

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Deriving social determinants of health from underserved populations is an important step in the process of improving the well-being of these populations and in driving policy improvements to facilitate positive change in health outcomes. Collection, integration, and effective use of clinical data for this purpose presents a variety of specific challenges. We assert that combining expertise from three distinct domains, specifically, medical, statistical, and computer and data science can be applied along with provenance-aware, self-documenting workflow tools. This combination permits data integration and facilitates the creation of reproducible workflows and usable (reproducible) results from the sensitive and disparate sources of clinical data that exist for underserved populations.

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Section: Dimensionality Reduction and Clusteringmentioning

confidence: 99%

Social determinants of health derived from people with opioid use disorder: Improving data collection, integration and use with cross-domain collaboration and reproducible, data-centric, notebook-style workflows

Markatou

Kennedy²,

Brachmann³

et al. 2023

Front. Med.

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show abstract

“…It is not sensitive with respect to noise, but it takes more time compared to K-means. [30] 3)CURE: Clustering Using Representatives is also a hierarchical based clustering technique. It selects well-scattered points from the cluster and then reduces them in the center of the cluster by a specified fraction.…”

Section: Text Clusteringmentioning

confidence: 99%

A brief survey of Text Mining and its applications

2021

IJETER

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The world came across the worst pandemic of all times in the year 2020 due to the outburst of Severe Acute Respiratory Syndrome Coronavirus-2 or Covid-19. All the questions about this outbreak were piled up and research was fast growing [1]. A study showing that in precisely just six months, substantial databases have been swamped with research articles, news, notes, and editorial related to coronavirus. It estimates that 23,634 distinctly published articles have been indexed on Web of Science and Scopus between 1 January and 30 June 2020. Imagine the data that is with us today!! Approximately 200,000 scholarly articles have been published related to Covid-19. This tells us that there is a need for simplifying search results to get answers to high priority questions for users specifically scientists. Currently, document clustering tools are being used in many areas. A similar clustering tool can be made particularly for Covid-19 which will help scientists and researchers get answers to high priority questions about this pandemic. In this paper, we are discussing about the process of text mining, text categorization and, text clustering. Also, a comparison of the algorithms used for clustering particularly in text data.

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“…In the current digital world, huge quantity of data is stored in variety of forms like image, text, audio, video and this may increase in the future [1]. The increase of digital information increases the demand of tools for analysis and discovers useful information.…”

Section: Introductionmentioning

confidence: 99%

Research of Clustering Algorithms using Enhanced Feature Selection

Thatha,

Babu,

Haritha

2019

IJEAT

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In Present situation, a huge quantity of data is recorded in variety of forms like text, image, video, and audio and is estimated to enhance in future. The major tasks related to text are entity extraction, information extraction, entity relation modeling, document summarization are performed by using text mining. This paper main focus is on document clustering, a sub task of text mining and to measure the performance of different clustering techniques. In this paper we are using an enhanced features selection for clustering of text documents to prove that it produces better results compared to traditional feature selection.

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Comparison of Algorithms for Document Clustering

Cited by 6 publications

References 7 publications

Social determinants of health derived from people with opioid use disorder: Improving data collection, integration and use with cross-domain collaboration and reproducible, data-centric, notebook-style workflows

Social determinants of health derived from people with opioid use disorder: Improving data collection, integration and use with cross-domain collaboration and reproducible, data-centric, notebook-style workflows

A brief survey of Text Mining and its applications

Research of Clustering Algorithms using Enhanced Feature Selection

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