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
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
Mining and Visualizing the Evolution of Subgroups in Social Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Data Clustering: 50 Years Beyond K-means
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
The Panda framework for comparing patterns
Data & Knowledge Engineering
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
Incremental Clustering of Mobile Objects
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Intrinsically dynamic network communities
Computer Networks: The International Journal of Computer and Telecommunications Networking
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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The study of evolution has become an important research issue, especially in the last decade, due to a greater awareness of our world’s volatility. As a consequence, a new paradigm has emerged to respond more effectively to a class of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory, and a transition detection algorithm. To demonstrate its feasibility and applicability we present real world case studies, using datasets extracted from Banco de Portugal and the Portuguese Institute of Statistics. We also test our approach in a benchmark dataset from TSDL. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions.