Page 179 - DCAP208_Management Support Systems

Management Support Systems

Notes
The community discovery task aims to discover coherent calling communities. Based on
the profile mining platform, a social network can be constructed using calls between
customers and the calling frequencies. Communities in the social network capture the
connectivity and similarity among customers. By considering the properties of the
communities, effective market campaign can be designed for targeted customers. For
example, the customers with broad social connections should be taken care specially.
We emphasize the following points in our demo. First, we present how we solve the
business tasks in mobile communication using novel data mining techniques. Second, we
use MobileMiner on real data to elaborate what can be done and how the data mining
techniques can be integrated in a business-driven model. Third, we show some examples
of what still cannot be done satisfactorily using the current data mining techniques, which
may motivate future research and development.

Technology and Novelty
Customer records are collected by the mobile communication base stations and fed into
MobileMiner as data streams, including customer moving trajectories and calling records.
A base station serves the cell phones in a specific region, and can detect a mobile customer
once she turns on her phone. Once the records are imported into the system, profile
mining is performed to generate user profiles for the upper layer data mining tasks.
Specifically, in the profile mining part, customers’ moving profiles or their frequent
moving patterns are constructed based on their moving records continuously. The core of
this task is to mine sequential patterns on data streams, which is challenging since there
can be many customers and the sliding window can be large. A customer’s moving profile
is formed using the set of closed sequential patterns that match the customer’s trajectory
and the profile is incrementally maintained. We developed a novel algorithm to mine and
incrementally maintain on fast data streams closed sequential patterns, which are
non-redundant representation of sequential patterns. An effective data structure is designed
to keep close sequential patterns in memory and various strategies are proposed to prune
search space aggresively. Based on the experiments on both real and synthetic databases,
our algorithm outperforms the best existing algorithms by a large margin. The details of
the techniques can be found in.
The mobile user segmentation module clusters customers according to their profiles. The
goal is to partition customers into groups such that the customers in a group are similar to
each other in moving patterns. Importantly, timestamps should be considered. Since each
point in a customer trajectory is associated with a timestamp, two trajectories are similar
only if they are close to each other in time dimension. The problem is formulated as
clustering trajectories in both space and time. The spatio-temporal patterns of clusters are
very useful for the company to allocate base stations effectively for specific customer
groups. Some related work clusters spatio-temporal patterns in bio-informatics. Here, we
adapt the algorithm to group 2-dimensional trajectories in different time stamps. The
main idea is to find biclusters with low mean squared residue through effectively iterative
search. The mean squared residue captures the variance of the set of trajectories in a
bicluster over time.

In mobile communication business, the social relationship among customers often plays
a significant role in marketing. For example, losing some customers with broad social
connections may cause customer churning. A social net- work among customers is
constructed. Each customer is represented by a node in the network. An edge is drawn to
connect two customers if they call each other over a certain number of times in the current
sliding window. A social community in the network is a set of nodes such that they are
Contd....

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