%0 Journal Article %1 singh_rising_2012 %A Singh, Param %A Raghuvanshi, Jitendra %D 2012 %J International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) %K text_mining %N 3 %T Rising of text mining technique : as unforeseen-part of data mining %U http://ijarcsee.org/index.php/IJARCSEE/article/view/80 %V 1 %X Text Data Mining or Knowledge-Discovery in Text (KDT) technique refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text. Text mining technique is a deviation on a countryside called data mining that tries to find interesting patterns from large databases; text mining also known as the Intelligent Text Analysis (ITA). Text mining is a young interdisciplinary field which draws on information retrieval, data mining, machine learning, statistics and computational linguistics. Text Mining Technique (TMT) is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. In this paper, we introduce the rising of Text Mining Technique as unforeseen-part of the Data Mining and Data Warehouse Methodologies; for improving its role, performances and productivities and also used in different research areas.

@article{singh_rising_2012, abstract = {Text Data Mining or Knowledge-Discovery in Text (KDT) technique refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text. Text mining technique is a deviation on a countryside called data mining that tries to find interesting patterns from large databases; text mining also known as the Intelligent Text Analysis (ITA). Text mining is a young interdisciplinary field which draws on information retrieval, data mining, machine learning, statistics and computational linguistics. Text Mining Technique (TMT) is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. In this paper, we introduce the rising of Text Mining Technique as unforeseen-part of the Data Mining and Data Warehouse Methodologies; for improving its role, performances and productivities and also used in different research areas.}, added-at = {2018-11-04T17:02:36.000+0100}, author = {Singh, Param and Raghuvanshi, Jitendra}, biburl = {https://www.bibsonomy.org/bibtex/25a06dbbedf29ea45abb615a7fcd09272/lepsky}, interhash = {9c8622f055ea148e76fcd630f2bdfaa8}, intrahash = {5a06dbbedf29ea45abb615a7fcd09272}, journal = {International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE)}, keywords = {text_mining}, number = 3, timestamp = {2018-11-07T09:15:37.000+0100}, title = {Rising of text mining technique : as unforeseen-part of data mining}, url = {http://ijarcsee.org/index.php/IJARCSEE/article/view/80}, volume = 1, year = 2012 }