Using K-Means Algorithm for Description Analysis of Text in RSS News Format

This article shows the use of different techniques for the extraction of information through text mining. Through this implementation, the performance of each of the techniques in the dataset analysis process can be identified, which allows the reader to recommend the most appropriate technique for...

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Autores:
De-La-Hoz-Franco, Emiro
Oviedo Carrascal, Ana Isabel
Ariza Colpas, Paola Patricia
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7459
Acceso en línea:
https://hdl.handle.net/11323/7459
http://doi.org/10.1007/978-981-32-9563-6_17
https://repositorio.cuc.edu.co/
Palabra clave:
RSS news’s format
Simple K-means
Bag of words
Stopwords
Text mining
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/7459
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Using K-Means Algorithm for Description Analysis of Text in RSS News Format
title Using K-Means Algorithm for Description Analysis of Text in RSS News Format
spellingShingle Using K-Means Algorithm for Description Analysis of Text in RSS News Format
RSS news’s format
Simple K-means
Bag of words
Stopwords
Text mining
title_short Using K-Means Algorithm for Description Analysis of Text in RSS News Format
title_full Using K-Means Algorithm for Description Analysis of Text in RSS News Format
title_fullStr Using K-Means Algorithm for Description Analysis of Text in RSS News Format
title_full_unstemmed Using K-Means Algorithm for Description Analysis of Text in RSS News Format
title_sort Using K-Means Algorithm for Description Analysis of Text in RSS News Format
dc.creator.fl_str_mv De-La-Hoz-Franco, Emiro
Oviedo Carrascal, Ana Isabel
Ariza Colpas, Paola Patricia
dc.contributor.author.spa.fl_str_mv De-La-Hoz-Franco, Emiro
Oviedo Carrascal, Ana Isabel
Ariza Colpas, Paola Patricia
dc.subject.spa.fl_str_mv RSS news’s format
Simple K-means
Bag of words
Stopwords
Text mining
topic RSS news’s format
Simple K-means
Bag of words
Stopwords
Text mining
description This article shows the use of different techniques for the extraction of information through text mining. Through this implementation, the performance of each of the techniques in the dataset analysis process can be identified, which allows the reader to recommend the most appropriate technique for the processing of this type of data. This article shows the implementation of the K-means algorithm to determine the location of the news described in RSS format and the results of this type of grouping through a descriptive analysis of the resulting clusters.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-11-24T16:26:09Z
dc.date.available.none.fl_str_mv 2020-11-24T16:26:09Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7459
dc.identifier.doi.spa.fl_str_mv http://doi.org/10.1007/978-981-32-9563-6_17
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7459
http://doi.org/10.1007/978-981-32-9563-6_17
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Palechor, F., De la hoz manotas, A., De la hoz franco, E., Colpas, P: Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems. J. Theor. Appl. Inf. Technol. 82(2) (2015)
2. Calabria-Sarmiento, J.C., et al.: Software applications to health sector: a systematic review of literature (2018)
3. Sen, T., Ali, M.R., Hoque, M.E., Epstein, R., Duberstein, P.: Modeling doctor-patient communication with affective text analysis. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 170–177. IEEE (2017)
4. Jeon, S.W., Lee, H.J., Cho, S.: Building industry network based on business text: corporate disclosures and news. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4696–4704. IEEE (2017)
5. Irfan, M., Zulfikar, W.B.: Implementation of fuzzy C-Means algorithm and TF-IDF on English journal summary. In: 2017 Second International Conference on Informatics and Computing (ICIC), pp. 1–5. IEEE (2017)
6. De-La-Hoz-Franco, E., Ariza-Colpas, P., Quero, J.M., Espinilla, M.: Sensor-based datasets for human activity recognition–a systematic review of literature. IEEE Access 6, 59192–59210 (2018)
7. Zhang, X., Yu, Q.: Hotel reviews sentiment analysis based on word vector clustering. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp. 260–264. IEEE (2017)
8. Vieira, A.S., Borrajo, L., Iglesias, E.L.: Improving the text classification using clustering and a novel HMM to reduce the dimensionality. Comput. Methods Programs Biomed. 136, 119–130 (2016)
9. Wu, H., Zou, B., Zhao, Y.Q., Chen, Z., Zhu, C., Guo, J.: Natural scene text detection by multi-scale adaptive color clustering and non-text filtering. Neurocomputing 214, 1011–1025 (2016)
10. Palechor, F.M., De la Hoz Manotas, A., Colpas, P.A., Ojeda, J.S., Ortega, R.M., Melo, M.P.: Cardiovascular disease analysis using supervised and unsupervised data mining techniques. JSW 12(2), 81–90 (2017)
11. Aradhya, V.M., Pavithra, M.S.: A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl. Comput. Inform. (2014)
12. Bharti, K.K., Singh, P.K.: Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering. Appl. Soft Comput. 43, 20–34 (2016)
13. Li, C.H.: Confirmatory factor analysis with ordinal data: comparing robust maximum likelihood and diagonally weighted least squares. Behav. Res. Methods 48(3), 936–949 (2016)
14. Melissa, A., François, R., Mohamed, N.: Graph modularity maximization as an effective method for co-clustering text data. Knowl.-Based Syst. 109(1), 160–173 (2016)
15. Mendoza-Palechor, F.E., Ariza-Colpas, P.P., Sepulveda-Ojeda, J.A., De-la-Hoz-Manotas, A., Piñeres Melo, M.: Fertility analysis method based on supervised and unsupervised data mining techniques (2016)
16. Wang, P., Xu, B., Xu, J., Tian, G., Liu, C.L., Hao, H.: Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification. Neurocomputing 174, 806–814 (2016)
17. Shafiabady, N., Lee, L.H., Rajkumar, R., Kallimani, V.P., Akram, N.A., Isa, D.: Using unsupervised clustering approach to train the Support Vector Machine for text classification. Neurocomputing 211, 4–10 (2016)
18. Zhang, W., Tang, X., Yoshida, T.: Tesc: an approach to text classification using semi-supervised clustering. Knowl.-Based Syst. 75, 152–160 (2015)
19. De França, F.O.: A hash-based co-clustering algorithm for categorical data. arXiv preprint arXiv:1407.7753 (2014)
20. Echeverri-Ocampo, I., Urina-Triana, M., Patricia Ariza, P., Mantilla, M.: El trabajo colaborativo entre ingenieros y personal de la salud para el desarrollo de proyectos en salud digital: una visión al futuro para lograr tener éxito (2018)
21. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)
22. Drineas, P., Frieze, A.M., Kannan, R., Vempala, S., Vinay, V.: Clustering in large graphs and matrices. In: SODA, vol. 99, pp. 291–299 (1999)
23. Meila, M., Shi, J.: Learning segmentation by random walks. In: NIPS, pp. 873–879 (2000)
24. Jain, A.K., Dubes, R.C.: Algorithms for clustering data (1988)
25. Guerrero Cuentas, H.R., Polo Mercado, S.S., Martinez Royert, J.C., Ariza Colpas, P.P.: Trabajo colaborativo como estrategia didáctica para el desarrollo del pensamiento crítico (2018)
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spelling De-La-Hoz-Franco, EmiroOviedo Carrascal, Ana IsabelAriza Colpas, Paola Patricia2020-11-24T16:26:09Z2020-11-24T16:26:09Z2019https://hdl.handle.net/11323/7459http://doi.org/10.1007/978-981-32-9563-6_17Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article shows the use of different techniques for the extraction of information through text mining. Through this implementation, the performance of each of the techniques in the dataset analysis process can be identified, which allows the reader to recommend the most appropriate technique for the processing of this type of data. This article shows the implementation of the K-means algorithm to determine the location of the news described in RSS format and the results of this type of grouping through a descriptive analysis of the resulting clusters.De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Oviedo Carrascal, Ana Isabel-will be generated-orcid-0000-0002-7105-7819-600Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2International Conference on Data Mining and Big Datahttps://link.springer.com/chapter/10.1007%2F978-981-32-9563-6_17RSS news’s formatSimple K-meansBag of wordsStopwordsText miningUsing K-Means Algorithm for Description Analysis of Text in RSS News FormatArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Palechor, F., De la hoz manotas, A., De la hoz franco, E., Colpas, P: Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems. J. Theor. Appl. Inf. Technol. 82(2) (2015)2. Calabria-Sarmiento, J.C., et al.: Software applications to health sector: a systematic review of literature (2018)3. Sen, T., Ali, M.R., Hoque, M.E., Epstein, R., Duberstein, P.: Modeling doctor-patient communication with affective text analysis. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 170–177. IEEE (2017)4. Jeon, S.W., Lee, H.J., Cho, S.: Building industry network based on business text: corporate disclosures and news. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4696–4704. IEEE (2017)5. Irfan, M., Zulfikar, W.B.: Implementation of fuzzy C-Means algorithm and TF-IDF on English journal summary. In: 2017 Second International Conference on Informatics and Computing (ICIC), pp. 1–5. IEEE (2017)6. De-La-Hoz-Franco, E., Ariza-Colpas, P., Quero, J.M., Espinilla, M.: Sensor-based datasets for human activity recognition–a systematic review of literature. IEEE Access 6, 59192–59210 (2018)7. Zhang, X., Yu, Q.: Hotel reviews sentiment analysis based on word vector clustering. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp. 260–264. IEEE (2017)8. Vieira, A.S., Borrajo, L., Iglesias, E.L.: Improving the text classification using clustering and a novel HMM to reduce the dimensionality. Comput. Methods Programs Biomed. 136, 119–130 (2016)9. Wu, H., Zou, B., Zhao, Y.Q., Chen, Z., Zhu, C., Guo, J.: Natural scene text detection by multi-scale adaptive color clustering and non-text filtering. Neurocomputing 214, 1011–1025 (2016)10. Palechor, F.M., De la Hoz Manotas, A., Colpas, P.A., Ojeda, J.S., Ortega, R.M., Melo, M.P.: Cardiovascular disease analysis using supervised and unsupervised data mining techniques. JSW 12(2), 81–90 (2017)11. Aradhya, V.M., Pavithra, M.S.: A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl. Comput. Inform. (2014)12. Bharti, K.K., Singh, P.K.: Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering. Appl. Soft Comput. 43, 20–34 (2016)13. Li, C.H.: Confirmatory factor analysis with ordinal data: comparing robust maximum likelihood and diagonally weighted least squares. Behav. Res. Methods 48(3), 936–949 (2016)14. Melissa, A., François, R., Mohamed, N.: Graph modularity maximization as an effective method for co-clustering text data. Knowl.-Based Syst. 109(1), 160–173 (2016)15. Mendoza-Palechor, F.E., Ariza-Colpas, P.P., Sepulveda-Ojeda, J.A., De-la-Hoz-Manotas, A., Piñeres Melo, M.: Fertility analysis method based on supervised and unsupervised data mining techniques (2016)16. Wang, P., Xu, B., Xu, J., Tian, G., Liu, C.L., Hao, H.: Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification. Neurocomputing 174, 806–814 (2016)17. Shafiabady, N., Lee, L.H., Rajkumar, R., Kallimani, V.P., Akram, N.A., Isa, D.: Using unsupervised clustering approach to train the Support Vector Machine for text classification. Neurocomputing 211, 4–10 (2016)18. Zhang, W., Tang, X., Yoshida, T.: Tesc: an approach to text classification using semi-supervised clustering. Knowl.-Based Syst. 75, 152–160 (2015)19. De França, F.O.: A hash-based co-clustering algorithm for categorical data. arXiv preprint arXiv:1407.7753 (2014)20. Echeverri-Ocampo, I., Urina-Triana, M., Patricia Ariza, P., Mantilla, M.: El trabajo colaborativo entre ingenieros y personal de la salud para el desarrollo de proyectos en salud digital: una visión al futuro para lograr tener éxito (2018)21. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)22. Drineas, P., Frieze, A.M., Kannan, R., Vempala, S., Vinay, V.: Clustering in large graphs and matrices. In: SODA, vol. 99, pp. 291–299 (1999)23. Meila, M., Shi, J.: Learning segmentation by random walks. In: NIPS, pp. 873–879 (2000)24. Jain, A.K., Dubes, R.C.: Algorithms for clustering data (1988)25. Guerrero Cuentas, H.R., Polo Mercado, S.S., Martinez Royert, J.C., Ariza Colpas, P.P.: Trabajo colaborativo como estrategia didáctica para el desarrollo del pensamiento crítico (2018)PublicationORIGINALUsing K.pdfUsing K.pdfapplication/pdf94964https://repositorio.cuc.edu.co/bitstreams/f733341a-3fec-43ae-89a1-460cf9b26499/downloaddca65cbfd4ff69063d3ca9d013e4c720MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/3ae1cd97-61c3-48eb-b2f2-26e432fcc6bd/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/e327d4df-1532-4bef-afb7-6b8d8a7f544d/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILUsing K.pdf.jpgUsing K.pdf.jpgimage/jpeg29199https://repositorio.cuc.edu.co/bitstreams/f4458f87-e82a-41aa-9a2a-ecb9aecf36ab/download184d585c1cc9dc250e0b2cdcc92ea8b7MD54TEXTUsing K.pdf.txtUsing 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