Text Data Mining Algorithm Combining CNN and DBM Models

The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Rong Dai
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/9242857986ec4afb9f87fa881a44e517
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9242857986ec4afb9f87fa881a44e517
record_format	dspace
spelling	oai:doaj.org-article:9242857986ec4afb9f87fa881a44e5172021-11-29T00:57:04ZText Data Mining Algorithm Combining CNN and DBM Models1875-905X10.1155/2021/2150488https://doaj.org/article/9242857986ec4afb9f87fa881a44e5172021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2150488https://doaj.org/toc/1875-905XThe special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining.Rong DaiHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Rong Dai Text Data Mining Algorithm Combining CNN and DBM Models
description	The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining.
format	article
author	Rong Dai
author_facet	Rong Dai
author_sort	Rong Dai
title	Text Data Mining Algorithm Combining CNN and DBM Models
title_short	Text Data Mining Algorithm Combining CNN and DBM Models
title_full	Text Data Mining Algorithm Combining CNN and DBM Models
title_fullStr	Text Data Mining Algorithm Combining CNN and DBM Models
title_full_unstemmed	Text Data Mining Algorithm Combining CNN and DBM Models
title_sort	text data mining algorithm combining cnn and dbm models
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/9242857986ec4afb9f87fa881a44e517
work_keys_str_mv	AT rongdai textdataminingalgorithmcombiningcnnanddbmmodels
_version_	1718407659811504128

Text Data Mining Algorithm Combining CNN and DBM Models

Ejemplares similares