Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
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
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Finding a Web Community by Maximum Flow Algorithm with HITS Score Based Capacity
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
DEMON: Mining and Monitoring Evolving Data
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
An Intuitive Framework for Understanding Changes in Evolving Data Streams
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Extracting evolution of web communities from a series of web archives
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
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In recent years, data streams have become ubiquitous in a variety of applications because of advances in hardware technology. Since data streams may be generated by applications which are time-changing in nature, it is often desirable to explore the underlying changing trends in the data. In this paper, we will explore and survey some of our recent methods for change detection. In particular, we will study methods for change detection which use clustering in order to provide a concise understanding of the underlying trends. We discuss our recent techniques which use micro-clustering in order to diagnose the changes in the underlying data. We also discuss the extension of this method to text and categorical data sets as well community detection in graph data streams.