Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Meaningful change detection in structured data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Monitoring Change in Mining Results
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams
Proceedings of the 2007 ACM symposium on Applied computing
Mining and Visualizing the Evolution of Subgroups in Social Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
The Panda framework for comparing patterns
Data & Knowledge Engineering
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
An event-based framework for characterizing the evolutionary behavior of interaction graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Quality indices for (practical) clustering evaluation
Intelligent Data Analysis
A Data Clustering Tool with Cluster Validity Indices
ICC '09 Proceedings of the 2009 International Conference on Computing, Engineering and Information
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Evolving clusters in gene-expression data
Information Sciences: an International Journal
Hi-index | 0.00 |
The study of evolution has become an important research issue, especially in the last decade, due to our ability to collect and store high detailed and time-stamped data. The need for describing and understanding the behavior of a given phenomena over time led to the emergence of new frameworks and methods focused on the temporal evolution of data and models. In this paper we address the problem of monitoring the evolution of clusters over time and propose the MEC framework. MEC traces evolution through the detection and categorization of clusters transitions, such as births, deaths and merges, and enables their visualization through bipartite graphs. It includes a taxonomy of transitions, a tracking method based in the computation of conditional probabilities, and a transition detection algorithm. We use MEC with two main goals: to determine the general evolution trends and to detect abnormal behavior or rare events. To demonstrate the applicability of our framework we present real world economic and financial case studies, using datasets extracted from Banco de Portugal Central Balance-Sheet Database and the The Data Page of New York University --Leonard N. Stern School of Business. The results allow us to draw interesting conclusions about the evolution of activity sectors and European companies.