02-11-2012, 06:02 PM
Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments
Mining Cluster-Based Temporal.pdf (Size: 2.19 MB / Downloads: 58)
INTRODUCTION
THE advancement of wireless communication techniques
and the popularity of mobile devices such as mobile
phones, PDA, and GPS-enabled cellular phones, have
contributed to a new business model. Mobile users can
request services through their mobile devices via Information
Service and Application Provider (ISAP) from anywhere
at any time [35]. This business model is known as
Mobile Commerce (MC) that provides Location-Based
Services (LBS) [34] through mobile phones. MC is expected
to be as popular as e-commerce in the future [27] and it is
based on the cellular network composed of several base
stations. The communication coverage of each base station
is called a cell [20] as a location area. The average distance
between two base stations is hundreds of meters and the
number of base stations is usually more than 10,000 in a
city. When users move within the mobile network, their
locations and service requests are stored in a centralized
mobile transaction database.
RELATED WORK
In this section, we review previous related studies, which
can be classified into four categories: mobile pattern mining
techniques, clustering methods, temporal pattern mining
techniques, and mobile behavior predictions.
In recent years, a number of studies have discussed the
usage of data mining techniques to discover useful rules/
patterns from WWW [25], transaction databases [1], [2], [3],
[13], [23], and mobility data [10], [19], [29], [33], [37]. Mining
association rules [1] are proposed to find important items in
a transaction database. Agrawal and Srikant [2] proposed
the Apriori algorithm to mine the association rules. Park
et al. [23] proposed the DHP algorithm to improve the
performance of association rule mining. Pei et al. [25]
proposed an algorithm named WAP-Mine to efficiently
discover web access patterns in web logs, using a tree-based
data structure without candidate generation.
System Framework
Fig. 2 shows the proposed system framework. Our system
has an “offline” mechanism for CTMSPs mining and an
“online” engine for mobile behavior prediction.Whenmobile
users move within the mobile network, the information
which includes time, locations, and service requests will be
stored in the mobile transaction database. Table 1 shows an
example of mobile transaction database which contains seven
records. In the offline data mining mechanism,wedesign two
techniques and the CTMSP-Mine algorithm to discover the
knowledge. First, we propose the CO-Smart-CAST algorithm
to cluster the mobile transaction sequences. In this algorithm,
we propose the LBS-Alignment to evaluate the similarity of
mobile transaction sequences. Second, we propose a GAbased
time segmentation algorithm to find the most suitable
time intervals.