Document Clustering Using Different Unsupervised Learning Approaches: A Survey

Afreen, Munazza; Badugu, Srinivasu

doi:10.1007/978-3-030-24318-0_71

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…This is widely used by banks for market segmentation [ 4 ]. Finally, automatic document clustering that organizes similar documents into classes (for purposes of improving information retrieval, for example) is gaining importance due to the increasing number of documents on the internet [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Glaz¹,

Haralambous²,

Kim-Dufor³

et al. 2021

J Med Internet Res

290

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Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Glaz¹,

Haralambous²,

Kim-Dufor³

et al. 2021

J Med Internet Res

290

View full text Add to dashboard Cite

show abstract

“…The news categorization is attracted to the concentration of the researchers from past decades. There were different text mining approaches adopted such as information retrieval [23], natural language processing [28], information extraction from the text, text summarization [29], supervised learning methods [30], unsupervised learning methods [31], probabilistic methods for text mining [32], text streams [33] and social media mining [34], opinion mining, sentiment analysis [35] and biomedical text mining [36] for the purpose categorization of text data. The clustering is one of the most popular data mining technique that is mostly used for unsupervised machine learning.…”

Section: Related Workmentioning

confidence: 99%

“…The most common technique that is used to label the data without any supervision that means this data does not have any true labels. The unsupervised machine learning [31] technique is used to perform training on the data without true labels that needs to learn from the features of the data based on similarity and dissimilarity measures to create clusters.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

et al. 2022

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The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means Clustering and Spectral Clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with Kmeans clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

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“…This is widely used by banks for market segmentation [4]. Finally, automatic document clustering that organizes similar documents into classes (for purposes of improving information retrieval, for example) is gaining importance due to the increasing number of documents on the internet [5].…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning and natural language processing in mental health: a systematic review

Glaz¹,

[]²,

Berrouiguet³

et al. 2019

Preprint

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Document Clustering Using Different Unsupervised Learning Approaches: A Survey

Cited by 3 publications

References 5 publications

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

Machine learning and natural language processing in mental health: a systematic review

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