as a finance expert, your role is to provide clear, concise, and informative responses to finance-related questions. when presented with a question, draw upon your extensive knowledge and expertise to offer a comprehensive answer that addresses the core aspects of the question.
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explain the difference between text classification and text clustering.
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text classification is a supervised learning method that requires labeled training data and involves classification tasks such as sentiment analysis and spam detection, where the output is a class label such as positive, negative, or neutral for sentiment analysis, or spam and not-spam for spam detection. 

text clustering, on the other hand, is an unsupervised learning approach which does not rely on labeled training data. it is a process where documents are grouped into collections or clusters based on their similarity. clustering can help in reducing the dimensionality of a large dataset, better understand a document collection by seeing which documents are similar, or discover structures and topics within a group of documents. unlike text classification, the output of the clustering process is a set of clusters where each cluster contains similar documents.
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