Soft Computing for XML Data Mining

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic Algorithms (GA), Artificial Neural Networks, Fuzzy Logic, Rough Sets and Support Vector Machines (SVM) when used in combination was found to be effective. Therefore, soft computing algorithms are used to accomplish data mining across different applications (Mitra S, Pal S K & Mitra P, 2002; Alex A Freitas, 2002). Extensible Markup Language (XML) is emerging as a de facto standard for information exchange among various applications of World Wide Web due to XML’s inherent data self-describing capacity and flexibility of organizing data. In XML representation, the semantics are associated with the contents of the document by making use of self describing tags which can be defined by the users. Hence XML can be used as a medium for interoperability over the Internet. With these advantages, the amount of data that is being published on the Web in the form of XML is growing enormously and many naïve users find the need to search over large XML document collections (Gang Gou & Rada Chirkova, 2007; Luk R et al., 2000).