Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
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
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
Conceptual clustering and its application to concept drift and novelty detection
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
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
An overview of social network analysis
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
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In this work we address the problem of monitoring the evolution of clusters, which became an important research issue in recent years due to our ability to collect and store data that evolves over time. The evolution is traced through the detection and categorization of transitions undergone by clusters' structures computed at different points in time. We adopt two main strategies for cluster characterization --representation by enumeration and representation by comprehension -, and propose the MEC (Monitor of the Evolution of Clusters) framework, which was developed along the lines of the change mining paradigm. MEC includes a taxonomy of various types of clusters' transitions, a tracking mechanism that depends on cluster representation, and a transition detection algorithm. Our tracking mechanism can be subdivided in two methods, devised to monitor clusters' transitions: one based on graph transitions, and another based on clusters' overlap. To demonstrate the feasibility and applicability of MEC we present real world case studies, using datasets from different knowledge areas, such as Economy and Education.