Structures Discovering for Optimizing External Clustering Validation Metrics

Spalenza, Marcos; Pirovani, Juliana P. C.; Oliveira, Elias

doi:10.1007/978-3-030-49342-4_15

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clustering validation is extremely significant when deciding the configuration of the parameters and methods to be used with particular results [40]. Here are some of the most used evaluation measurements in the study, that are used to verify the consistency of the proposed clustering methods in this research.…”

Section: Clustering Validation Metricsmentioning

confidence: 99%

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

2021

View full text Add to dashboard Cite

The internet has become one of the main sources of news spread as it unleashed the information dissemination space, where the news websites express opinions on entities while also reporting on recent or unusual security risks. Recently, many research studies have focused on sentimental reflection on the views and impressions of people utilising natural language processing and analytical linguistics. Therefore, we have collected corpus from popular Arabic websites that publish articles related to recent security issues, and we provide light weight preprocessing techniques where data is term matrix is transformed. We also present an intensive lexical-driven data analysis with visualised data views, as our topic modelling technique can effectively extract significant topics from all the collected text from different websites. Our experiments validate the k-means clustering algorithm with and without the latent Dirichlet allocation topic modelling method, and we adopted various validation techniques to measure the topic clustering internally and externally. As shown in the experiments' results, our proposed combined method has a high round index rate of 87.2%, with a large number of topics and clusters. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Clustering Validation Metricsmentioning

confidence: 99%

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

2021

View full text Add to dashboard Cite

show abstract

Recommendation Filtering à la carte for Intelligent Tutoring Systems

Silva

Spalenza

Bourguet

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

Real-world data in rheumatoid arthritis: patient similarity networks as a tool for clinical evaluation of disease activity

Janca,

Ochodkova,

Kriegova

et al. 2023

Appl Netw Sci

View full text Add to dashboard Cite

Hospital databases provide complex data on individual patients, which can be analysed to discover patterns and relationships. This can provide insight into medicine that cannot be gained through focused studies using traditional statistical methods. A multivariate analysis of real-world medical data faces multiple difficulties, though. In this work, we present a methodology for medical data analysis. This methodology includes data preprocessing, feature analysis, patient similarity network construction and community detection. In the theoretical sections, we summarise publications and concepts related to the problem of medical data, our methodology, and rheumatoid arthritis (RA), including the concepts of disease activity and activity measures. The methodology is demonstrated on a dataset of RA patients in the experimental section. We describe the analysis process, hindrances encountered, and final results. Lastly, the potential of this methodology for future medicine is discussed.

show abstract

Structures Discovering for Optimizing External Clustering Validation Metrics

Cited by 3 publications

References 12 publications

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

Recommendation Filtering à la carte for Intelligent Tutoring Systems

Real-world data in rheumatoid arthritis: patient similarity networks as a tool for clinical evaluation of disease activity

Contact Info

Product

Resources

About