A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Fundamentals of Cellular Network Planning and Optimisation: 2G/2.5G/3G... Evolution to 4G
Fundamentals of Cellular Network Planning and Optimisation: 2G/2.5G/3G... Evolution to 4G
The Dimensions of Tacit & Explicit Knowledge: A Description and Measure
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Clustering
Cluster Analysis
ACM Computing Surveys (CSUR)
Handbook of Statistical Analysis and Data Mining Applications
Handbook of Statistical Analysis and Data Mining Applications
Radio Access Networks for UMTS: Principles and Practice
Radio Access Networks for UMTS: Principles and Practice
A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering
Expert Systems with Applications: An International Journal
A distribution-based approach to anomaly detection and application to 3G mobile traffic
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Clustering ellipses for anomaly detection
Pattern Recognition
SP 800-94. Guide to Intrusion Detection and Prevention Systems (IDPS)
SP 800-94. Guide to Intrusion Detection and Prevention Systems (IDPS)
Advanced analysis methods for 3G cellular networks
IEEE Transactions on Wireless Communications
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In comparison to the earlier telecommunications networks, present-day 3rd generation (3G) networks are able to provide more complex and detailed performance data, such as distributions of channel quality indicators. However, the operators lack proper methods and tools to efficiently utilize these data in monitoring and analysis of the networks. In this article, we apply fuzzy computing to channel quality measurement distributions to get the network elements (cells) clustered into groups of similar behavior. Groups and their descriptors provide valuable information for a radio expert, who is responsible for hundreds or thousands of elements. We introduce a fuzzy inference system based on features extracted from the distributional data and provide interpretation of the found categories to demonstrate their usability on network monitoring. Additionally we present how fuzzy clustering can be used in network performance monitoring and anomaly detection. Finally, we introduce further analysis on how time dimension is an interesting perspective to analyze network element behavior. All the achieved results were discussed with radio network performance experts who found them informative and useful.