Mining Matrix Pattern from Mobile Users - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 233

introduction
Data mining (Agrawal & Srikant, 1994, 1995; Chen & Liu, 2005; Xiao, Yao, & Yang, 2005) is the field
of research which aims to extract useful and interesting patterns out from source datasets supplied to the
algorithm. Data mining is an emerging field which allows organisations such as business and govern-
ment who have a huge amount of datasets stored in very large database to be able to benefit from the
algorithms by converting datasets into patterns and eventually studied and becomes useful knowledge.
Data mining is still an ongoing research, and previously available outcomes from data mining include
association rules, sequential patterns which derives useful patterns by analysing market basket (Agrawal
& Srikant, 1994, 1995), which is the list of items customers buy in a supermarket. Other previously
proposed methods in data mining includes time series analysis (Barbar'a, Chen, & Nazeri, 2004; Han,
Dong, & Yin, 1999; Han, Gong, & Yin 1998), brain analysis (Claude, Daire, & Sebag, 2004), Web log
pattern analysis (Christophides, Karvounarakis, & Plexousakis, 2003; Eirinaki & Vazirgaiannis, 2003;
Wilson & Matthews, 2004), increasing overall efficiency of data mining in very large databases (Han,
Pei, & Yin, 2000; Li, Tang, & Cercone, 2004; Thiruvady & Webb, 2004), data mining on data warehouses
(Tjioe & Taniar, 2005), security of private data in data mining (Oliveira, Zaiane, & Saygin, 2004) and
spatial, location dependent data mining (Hakkila & Mantyjarvi, 2005; Koperski & Han, 1995; Lee, Xu,
Zheng, & Lee, 2002; Tse, Lam, Ng, & Chan, 2005).
Mobile user data mining (Goh & Tanair, 2004a, 2004b, 2005; Lee, Xu, Zheng, & Lee, 2002; Lim,
Wang, Ong, et al., 2003) is an extension of data mining which specializes in looking at how useful
patterns can be derived from the raw datasets collected from mobile users. In a mobile environment,
two types of entities can usually be found: static nodes, which are fixed entities such as the wireless
access points, and mobile nodes, which are the mobile entities which have the flexibility to move along
in the environment, such as the personal digital assistant, mobile phones, and laptop computers. The
raw datasets from mobile users comes from the physical movement logs of mobile users, the items that
mobile users purchased over time, the location of static nodes and their properties and the context in
which the mobile users went into over a timeframe.
This chapter aims to propose a new method for finding relationships among two locations in a mobile
environment in order to determine the nature of how mobile users visit them. This mobile user data mining
method could reduce the consumption of resources for mobile user data mining by using the strategy of
gathering data only to the extent that is relevant for the desired accuracy. In this proposed method, the
covered area for mobile user data mining is first surveyed and mapped into a matrix, which could be a
dense or sparse matrix depending on the amount of items marked into the matrix. Once this mapping
is done, the matrix is used when the mobile user starts visiting the physical locations and the visiting
behaviours are recorded based on the position in the matrix which the mobile users have contacted.
This data is then used for data mining purposes, thus reducing the requirement for constantly identify-
ing and gathering of the latest position information of the mobile users. The elimination of the need to
constantly identify the mobile nodes reduces the performance cost required to gather the source data. By
using a matrix to identify the mobile users, the behaviours are then totally marked on the matrix itself
using simple markers. The chapter also further extends the proposed method by using a multi-layered
matrix, which is required to accommodate mining the relationships among two contexts.
The motivation for matrix pattern evolves from mobile user data mining (Goh & Taniar, 2004a; Lee,
Xu, Zheng, & Lee, 2002; Lim, Wang, Ong, et al., 2003). First, frequency pattern (Goh & Taniar, 2004a)
is developed which finds out the group characteristics among mobile users based on their frequency of