Page 180 - DCAP208_Management Support Systems

Unit 10: Data Mining Tools and Techniques

Notes
relatively well connected to each other and much less connected to the other nodes in the
network. Some previous work discovers communities in a network. In this application,
the connection weights on edges in graphs should be considered. The core set is a set of
customers whom are frequently called by other customers. The affiliated customers are
the customers surrounding the core with different layers. We use the calling frequencies
as the weights in the process of finding core customers and ranking affiliated customers.
To control the granularity of the discovering communities, a merging schema is used to
merge similar communities to get coarser results.
Demo Scenario
Our demo consists of three parts. First, we showcase how we integrate the state-of-the-art
data mining techniques into a business framework and building MobileMiner as a business
solution. Second, we illustrate how the underlying data mining techniques affect business
analysis. Last, we present some interesting observations found from real data which
unfortunately still cannot be handled by the existing techniques. Such case studies may
motivate novel data mining research and development.
Techniques Meeting Business Requirements
We will demonstrate some common business analysis tasks in mobile communication
companies, including customer segmentation for mobile service bases deployment, and
calling community discovery for marketing campaign design. For example, the user
interface of the mobile user segmentation module is not a simple list of the users grouped
in each cluster. MobileMiner visualizes the user groups by showing their moving patterns,
each group in a different color. Moreover, the moving patterns are shown in temporal
order with a local map as the background. With this information, analysts can make
informative decisions about how to deploy mobile base stations more effectively. We
will also show how calling community discovery techniques help companies to design
marketing campaign. The graph mining results are presented properly in a business
driven way. Based on the discovered knowledge, business analysts can identify targeted
customers in an effective way.
Sharpening Business Analysis by Tuning Techniques
Data mining techniques need to be tuned to make business analysis effective. To understand
how well the data mining techniques in MobileMiner work in practice, we use a real
mobile communication data set to show some interesting mining results.
To demonstrate the tuning needs, we will show how the parameters of our sequential
pattern mining algorithms may affect the mining results. Moreover, it is important that
the user interface can help business analysts to tune the underlying data mining methods.
A business analyst can interact with the social community visualization to tune the
parameters of the social network construction such as the call frequency threshold and the
time window.
Opportunities for Future Research
Mobile communication is a fast growing industry. We demonstrate some patterns found
yet from real data by human analysts but cannot be found using the data mining techniques.
For example, a new service of low calling charge by the company may negatively effect
the sales of another service such as monthly SMS. It is critical to analyze whether such a
new service overall improves the business and thus whether it should be introduced.
Usually, this decision is based on the experiential analysis on both potential profits and
potential customers. This task can be modeled as a hypothesis mining problem, which is
highly demanded in business but has not been systematically studied in a practical setting.
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