Data Mining

(redirected from Text mining)
Also found in: Dictionary, Thesaurus, Medical, Encyclopedia, Wikipedia.

Data Mining

The practice of looking for a pattern in a large amount of seemingly random data. Data mining is usually done with a computer program and helps in marketing. That is, a company can look at the (publicly available) purchase patterns of a person or group of persons and determine what products to direct at them.
Farlex Financial Dictionary. © 2012 Farlex, Inc. All Rights Reserved
References in periodicals archive ?
Therefore, in this research, we proposes a practical text mining methodology for the science and technology trend analysis, and conducts mining tasks such as making an ontology, topic analysis, and network relationship analysis.
The era of applying text mining approaches to biology and biomedical fields came into existence in 1999.
Figure 2 defines CompleteText mining model to process the text mining from selecting the targeted dataset to pattern discovery and interpretation of evaluated knowledge [6].Facts are stored in relational database [8] for the analysis.
Knowledge base utilized in [6] empowers huge quantity of machine learning methods for diverse number of text mining jobs.
There are seven specialties within text mining that have different objectives.
* Text Mining e ideal para inspecionar mudancas no mercado, ou para identificar ideias.
This requires expertise in areas such as text mining, social network analytics and data science.
* knowledge extraction from text, text mining combined with social network analysis.
CURRENT ANTI-FRAUD TECHNOLOGY Automated Red Flags 64% Scoring Capability 60% Link Analysis 57% Workflow Routing 43% Text Mining 40% Predictive Modeling 40% Geographic Data Maps 23% Data Source: Coalition Against Insurance Fraud.
The first attempts at text mining of the biomedical literature date back to 1998.
However, these languages generally are not optimized for the purpose of text mining. In other words, they usually consider queries as individuals and only return raw results for each query.
Since much of knowledge is in the form of unstructured information, a chapter on Text mining could have been a useful addition.