A large amount of information is stored in databases, in intranets or in Internet. This information is organised in documents or in text documents. The difference depends on the fact if pictures, tables, figures, and ...
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ISBN:
(纸本)3540423591
A large amount of information is stored in databases, in intranets or in Internet. This information is organised in documents or in text documents. The difference depends on the fact if pictures, tables, figures, and formulas are included or not. The common problem is to find the desired piece of information, a trend, or an undiscovered pattern from these sources. The problem is not a new one. Traditionally the problem has been considered under the title of information seeking, this means the science how to find a book in the library. Traditionally the problem has been solved either by classifying and accessing documents by Dewey Decimal Classification system or by giving a number of characteristic keywords. The problem is that nowadays there axe lots of unclassified documents in company databases and in intranet or in Internet. First one defines some terms. Text filtering means an information seeking process in which documents are selected from a dynamic text stream. Text mining is a process of analysing text to extract information from it for particular purposes. Text categorisation means the process of clustering similar documents from a large document set. All these terms have a certain degree of overlapping. Text mining, also know as document information mining, text datamining, or knowledge discovery in textual databases is an merging technology for analysing large collections of unstructured documents for the purposes of extracting interesting and non-trivial patterns or knowledge. Typical subproblems that have been solved axe language identification, feature selection/extraction, clustering, natural language processing, summarisation, categorisation, search, indexing, and visualisation. These subproblems are discussed in detail and the most common approaches axe given. Finally some examples of current uses of text mining are given and some potential application areas are mentioned.
Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many machinelearning problems [4, 16]. However, the extent of such improvement depends greatly o...
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