A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing Maps
Mining Changes for Real-Life Applications
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Exploratory multilevel hot spot analysis: Australian taxation office case study
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Visualizing transactional data with multiple clusterings for knowledge discovery
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Analysis of cluster migrations using self-organizing maps
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Decomposing the global financial crisis: A Self-Organizing Time Map
Pattern Recognition Letters
Letters: Clustering of the Self-Organizing Time Map
Neurocomputing
| Hi-index | 0.00 |
In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that the methods are capable of revealing changes in cluster strucure and membership, corresponding to known changes in economic fortunes of countries.