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 following 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.
|Title of host publication||Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications|
|Number of pages||28|
|Publication status||Published - 2008|