Compare Data Mining and Text Mining

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Data mining	Text mining
1.Data Mining (DM) is the practice of examining large pre-existing databases in order to generate new information.	1.Text mining refers to the process of deriving high-quality information from text.
2.Data mining is concerned with important aspects related to both database techniques and AI/machine learning mechanisms.	2.Text mining is concerned with the organization and retrieval of information from a large number of text-based documents.
3.It supports the mining of mixed data.	3.It supports the mining of only text, they do not support mixed structured and unstructured data
4.It supports mining of more than one text column at once.	4.Only a single column of text can be mined at one time.
5.Data mining system can be categorized according to various criteria, as follows :	5.In general, the major approaches, based on the kinds of data they take as input, are:
1,Classification according to the kinds of databases mined, 2.Classification according to the kinds of knowledge mined, 3.Classification according to the kinds of techniques utilized, 4.Classification according to the application adapted.	1.the keyword-based approach: where the input is a set of keywords or terms in the documents, i.A simple keyword-based approach may only discover relationships at a relatively shallow level, ii.It may not bring much deep understanding to the text. 2.,The tagging approach: where the input is a set of tags, i.The tagging approach, ii.may rely on tags obtained by manual tagging (which is costly and is unfeasible for large collections of documents), 3.The information-extraction approach: which inputs semantic information, such as events, facts, or entities uncovered by information extraction.

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