Text mining of Document using Keyphrase Extraction and Artificial Neural Network Approach for Machine Learning
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Abstract
Text mining is process of finding meaningful information from large amount of unstructured text documents. Key phrases are an important means of document summarization, clustering, and topic search. Only a small minority of documents have author-assigned keyphrases, and manually assigning keyphrases to existing documents is very tedious. Therefore it is highly desirable to automate the keyphrase extraction process. Kea-mean clustering Algorithm is combination of k-mean and keyphrase extraction algorithm. In Kea-means algorithm, documents are clustered into several groups like K-means, but the number of clusters is determined automatically by using the extracted keyphrases. Set of training documents and machine learning is used to determine phrases are keyphrase or not. Cluster analysis is required in text mining for grouping objects. Keyphrase extraction algorithm returns several keyphrases from the source documents. The Kea-means clustering algorithm provides easy and efficient way to extract documents from document resources.
Keywords: Text mining, Keyphrase extraction, clustering, Categorization, Neural Networks
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