A Review: Web Content Mining Techniques

Shah, Priyanka; Pandit, Hardik B.

doi:10.1007/978-981-16-2641-8_15

Cited by 2 publications

(1 citation statement)

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“…In the future online recommender system which checks acceptance level by the real users to provide recommendations can be implemented. From these, [20] proposed a review of the various techniques of web content mining. There are three different types of web mining, content mining, usage mining, and the final one is structure mining, where in the paper importance is given to web content mining.…”

Section: Literature Reviewmentioning

confidence: 99%

Web Page Recommendation System Based on Text and Image Pattern Extraction Classification Model

2023

IJIES

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Classification and analysis are improved factors for the real-time automation system. We need to analyze the attribute level in the area to predict the type of web page that can be predicted. This paper proposed a novel feature prediction model and the classification algorithm to estimate the attribute level of data and its probabilistic image to analyze the type of content that can be predicted on the page. This is to classify the different types of web pages which is better to perform the web page recommendation. For this process, dynamic lexical magnitude pattern (DLMP) system and correlated boltzmann machines (CBM) based classification model are used to analyze the attribute and image of the page area. The dataset consists of collections of attributes and images at various data samples for a page. In the DLMP-PGDT-based feature analysis method; the extract of the attribute and image in various texture patterns are analyzed and framed as the pattern for the given dataset. Then from that, an improved neural network architecture based on the block probability analysis is used to classify the data pattern to predict the class of web page according to the features of the dataset. This classification model assists were to recommend the content. The result analysis presents the comparison result of the proposed work with the web page classification technique like TrAdaBoost algorithm where proposed technique achieved accuracy of about 98%.

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Section: Literature Reviewmentioning

confidence: 99%