M. Tech. Electrical Engineering. / Telecommunication databases contain billions of records and are among the largest in the world, reaching around 30 terabits (30 trillion bits). Data mining is a proven solution for analysing such large volumes of data where traditional methods of turning data into knowledge are impractical. However, the increasing size (scalability), complexity (complex data types) and high dimensionality of telecommunication databases pose a significant challenge for conventional data mining approaches. In this dissertation, a matrix decomposition method (Singular Value Decomposition or SVD) is used to improve data mining for subscriber classification in mobile cellular networks. Using a large real mobile network dataset, the performance of a standard data mining approach (for clustering analysis) is evaluated when it is used with, and without, matrix decomposition. The proposed approach decreases the computational cost, for a given size of data (in terms of number of rows and columns). We also demonstrate improvement of the quality of clusters, yielding the following improvements in clustering assessment indices: 2.45% in Jaccard score, 3.5% in purity, and 1.35% in efficiency. Subscribers with different behaviours in the network are classified on the basis of various features; SVD analysis on their voice, text message, and data usage patterns are also performed. The proposed data mining model can be used for business intelligence activities such as customer segmentation, traffic modelling and social network analysis.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:tut/oai:encore.tut.ac.za:d1000549 |
| Date | January 2011 |
| Creators | João, Zolana Rui. |
| Contributors | Mzyece, Mjumo., Kurien, Anish Mathew. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | |
| Rights | © 2011 Tshwane University of Technology |
Page generated in 0.0023 seconds