Leading the Way in Efficient Web Content Mining through Advanced Classification and Clustering Techniques
Keywords:
Data Mining, Heterogeneous networks, Knowledge Discovery, Text mining, Web miningAbstract
The clustering techniques in online content mining for knowledge discovery is the main topic of the abstract for the article "Clustering Techniques in Knowledge Discovery for Web Content Mining". The application of association rule mining, sequential pattern discovery, and clustering as data mining techniques for knowledge extraction is mentioned.
When the data comes from the online, web mining—the process of obtaining information from web data—is referred to as a subset of knowledge discovery from databases (KDD). A particular kind of web mining called web use mining (WUM) seeks to identify, assess, and make use of hidden knowledge from online data sources. Data from user registration forms, server access logs, user profiles, and transactions are used in web use mining.
It is mentioned that one technique utilized in online content mining for knowledge discovery is clustering algorithms. In the context of online content mining, clustering is the process of assembling comparable data points into groups according to their shared traits or patterns. Clustering may be used to find page sets, page sequences, and page graphs.
The use of text analysis methods for knowledge discovery from unstructured materials, including feature extraction, theme indexing, clustering, and summarization, is also mentioned in the abstract. Press releases, emails, notes, contracts, government reports, and news feeds are just a few of the documents from which valuable information may be extracted thanks to these strategies.
An overview of the use of clustering algorithms in knowledge discovery for online content mining is given in the abstract overall. It highlights the use of text analysis tools to extract knowledge from unstructured documents and the clustering approach in online use mining.
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