Mining Higher-Order Association Rules from Distributed Named Entity Databases

The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed databases has grown, Distributed Association Rule Mining (D-ARM) algorithms have been developed. These algorithms, however, assume that the databases are either horizontally or vertically distributed. In the special case of databases populated from information extracted from textual data, existing D-ARM algorithms cannot discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. In this article we present D-HOTM, a framework for Distributed Higher Order Text Mining. Unlike existing algorithms, D-HOTM requires neither full knowledge of the global schema nor that the dist...

• Type: Conference
• Year: 2007
• Where: ISI
• Authors: Shenzhi Li, Christopher D. Janneck, Aditya P. Belapurkar, Murat Can Ganiz, Xiaoning Yang, Mark Dilsizian, Tianhao Wu, John M. Bright, William M. Pottenger
• Keywords: Distributed Database, Effective Distributed Information, ISI 2007, Security Privacy, Textual Data