Semi-Structured Data Structured Data Conversion Using Data Mining Methods
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2017, Vol 4, Issue 10
Abstract
Emerging technologies of semi-structured data have attracted a wide attention like networks, e-commerce, information retrieval and databases. In these applications, the data are modeled not as static collections but as transient data streams, where the data source is an unbounded stream of individual data items. It is becoming increasingly popular to send heterogeneous and ill-structured data through networks. Since traditional database technologies are not directly applicable to such data streams, it is important to study efficient information extraction methods for semi-structured data. Hence there has been increasing demand for automatic methods for extracting useful information, particularly, for discovering rules or patterns from large collection of semi-structured data, namely, semi-structured data mining. We introduce a class of simple combinatorial patterns over texts such as proximity phrase association patterns and ordered and unordered tree patterns modeling unstructured texts and semi-structured data on the Web. In addition with, we consider the problem of finding the patterns that optimize a given statistical measure within the whole class of patterns in a large collection of unstructured texts. For these classes of patterns, we develop fast and robust text mining algorithms based on techniques in computational geometry, string matching, and combinatorial optimization. We successfully implemented the developed text and semi-structured mining algorithms with experiments on interactive document browsing in a large text database, keyword and common structure discovery from Web.
Authors and Affiliations
B. Suchitra
Keywords
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