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Difference Between Data Mining and Text Mining

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Data Mining: 
Data mining is the process of finding patterns and extracting useful data from large data sets. It is used to convert raw data into useful data. Data mining can be extremely useful for improving the marketing strategies of a company as with the help of structured data we can study the data from different databases and then get more innovative ideas to increase the productivity of an organization. Text mining is just a part of data mining. 

Data mining is the process of discovering patterns and knowledge from large amounts of data. It involves the use of various techniques such as machine learning, statistical analysis, and database management to extract insights and information from data.

Data mining can be applied to a wide range of data types such as numerical, categorical, and image data. It can be used for tasks such as prediction, classification, clustering, and association rule mining. Data mining can be applied in various industries such as finance, healthcare, retail, and manufacturing.

Text Mining: 
Text mining is basically an artificial intelligence technology that involves processing the data from various text documents. Many deep learning algorithms are used for the effective evaluation of the text. In text mining, the data is stored in an unstructured format. It mainly uses the linguistic principles for the evaluation of text from documents. 

Text mining, also known as text data mining, is a specific application of data mining that deals with unstructured text data. It involves the use of natural language processing (NLP) techniques to extract useful information and insights from large amounts of unstructured text data, such as documents, emails, and social media posts. Text mining can be used for tasks such as sentiment analysis, named entity recognition, and topic modeling.

Text mining, on the other hand, is mainly used to extract useful information and insights from unstructured text data. This can include extracting named entities, such as people and organizations, from a document, or identifying sentiment, such as positive or negative, from a social media post. Text mining can be used in various fields such as natural language processing, information retrieval, and social media analysis.
 

Data-Mining-vs-Text-Mining

Below is a table of differences between Data Mining and Text Mining: 
 

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S.No. Data Mining Text Mining
1. Data mining is the statistical technique of processing raw data in a structured form. Text mining is the part of data mining which involves processing of text from documents.
2. Pre-existing databases and spreadsheets are used to gather information. The text is used to gather high quality information.
3. Processing of data is done directly. Processing of data is done linguistically.
4. Statistical techniques are used to evaluate data. Computational linguistic principles are used to evaluate text.
5. In data mining data is stored in structured format. In text mining data is stored in unstructured format.
6. Data is homogeneous and is easy to retrieve. Data is heterogeneous and is not so easy to retrieve.
7. It supports mining of mixed data. In text mining, mining of text is only done.
8. It combines artificial intelligence, machine learning and statistics and applies it on data. It applies pattern recognizing and natural language processing to unstructured data.
9. It is used in fields like marketing, medicine, healthcare. It is used in fields like bioscience and customer profile analysis.

Last Updated : 14 Feb, 2023
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