Mining Matrix Pattern from Mobile Users - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 232

Chapter XIII
Mining Matrix Pattern from
Mobile Users
John Goh
Monash University, Australia
David Taniar
Monash University, Australia
aBstract
Mobile user data mining is about extracting knowledge from raw data collected from mobile users. There
have been a few approaches developed, such as frequency pattern (Goh & Taniar, 2004), group pattern
(Lim, Wang, Ong, et al., 2003; Wang, Lim, & Hwang, 2003), parallel pattern (Goh & Taniar, 2005)
and location dependent mobile user data mining (Goh & Taniar, 2004). Previously proposed methods
share the common drawbacks of costly resources that have to be spent in identifying the location of the
mobile node and constant updating of the location information. The proposed method aims to address
this issue by using the location dependent approach for mobile user data mining. Matrix pattern looks
at the mobile nodes from the point of view of a particular fixed location rather than constantly follow-
ing the mobile node itself. This can be done by using sparse matrix to map the physical location and
use the matrix itself for the rest of mining process, rather than identifying the real coordinates of the
mobile users. This allows performance efficiency with slight sacrifice in accuracy. As the mobile nodes
visit along the mapped physical area, the matrix will be marked and used to perform mobile user data
mining. The proposed method further extends itself from a single layer matrix to a multi-layer matrix in
order to accommodate mining in different contexts, such as mining the relationship between the theme of
food and fashion within a geographical area, thus making it more robust and flexible. The performance
and evaluation shows that the proposed method can be used for mobile user data mining.