with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mini...

Text mining, also known as knowledge discovery from text, and document information mining, refers to the process of extracting interesting patterns from very large text corpus for the purposes of discovering knowledge. Text mining is an interdisciplinary field involving information retrieval, text understanding, information extraction, clustering, categorization, visualization, database technol...

Text mining, as one major school in Knowledge Discovery in Data (KDD), mines hidden patterns, rules, regularities and trends from textual data / non-database-data (i.e., text files, web documents, etc.). It is quite different from data mining (another well-known major school in KDD): the data structure of texts, dealt by text mining, is considered implicit, whereas traditional database-data, de...

Text mining is an effective means of detecting potentially useful knowledge from large text documents. However conventional text mining technology cannot achieve high accuracy, because it cannot effectively make use of the semantic information of the text. Ontology provides theoretical basis and technical support for semantic information representation and organization. This paper improves the ...

Data mining and text mining can help decision makers obtain business intelligence and make informed decisions, but using one of them gives us only a partial picture. The application of data mining can lead to questions that cannot be answered with only numbers. Therefore, decision makers will need text mining to drill the textual data to find explanations for numbers. On the other hand, the app...