Compared with traditional association rule mining in the structured world (e.g. Relational Databases), mining from XML data is confronted with more challenges due to the inherent flexibilities of XML in both structure and semantics. The major challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger size for each data element. In order to make XML-enabled association rule mining truly practical and computationally tractable, we propose a practical model for mining association rules from XML documents and demonstrate the usability and effectiveness of model through a set of experiments on real-life data
XML has become the standard for data representation on the web. This expansion in reputation has pro...
In recent times, the mining of association rules from XML databases has received attention because o...
Extracting information from semistructured documents is a very hard task, and is going to become mor...
With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to ext...
XML-enabled association rule framework [FDWC03] extends the notion of associated items to XML fragme...
Abstract—The inherent flexibilities of XML in both structure and semantics makes mining from XML dat...
In this paper, we propose a new framework, called XLogic- Miner, to mine association rules from XML ...
An XML enabled framework for representation of association rules in databases was first presented in...
The increasing amount of XML datasets available to casual users increases the necessity of investiga...
XML has become the standard for data representation on the internet. This expansion in reputation ha...
The increasing amount of very large XML datasets available to casual users is a challenging problem ...
An XML enabled framework for representation of association rules in databases was first presented in...
measures, performance measures Data mining algorithms are designed to extract interesting informatio...
Abstract- With the growing usage of XML in the World Wide Web and elsewhere as a standard for the ex...
XML has become the standard for data representation on the Web. This expansion in reputation has pro...
XML has become the standard for data representation on the web. This expansion in reputation has pro...
In recent times, the mining of association rules from XML databases has received attention because o...
Extracting information from semistructured documents is a very hard task, and is going to become mor...
With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to ext...
XML-enabled association rule framework [FDWC03] extends the notion of associated items to XML fragme...
Abstract—The inherent flexibilities of XML in both structure and semantics makes mining from XML dat...
In this paper, we propose a new framework, called XLogic- Miner, to mine association rules from XML ...
An XML enabled framework for representation of association rules in databases was first presented in...
The increasing amount of XML datasets available to casual users increases the necessity of investiga...
XML has become the standard for data representation on the internet. This expansion in reputation ha...
The increasing amount of very large XML datasets available to casual users is a challenging problem ...
An XML enabled framework for representation of association rules in databases was first presented in...
measures, performance measures Data mining algorithms are designed to extract interesting informatio...
Abstract- With the growing usage of XML in the World Wide Web and elsewhere as a standard for the ex...
XML has become the standard for data representation on the Web. This expansion in reputation has pro...
XML has become the standard for data representation on the web. This expansion in reputation has pro...
In recent times, the mining of association rules from XML databases has received attention because o...
Extracting information from semistructured documents is a very hard task, and is going to become mor...