| 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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