Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information 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
An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Temporal Features in Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient Mining of Association Rules in Large Dynamic Databases
BNCOD 16 Proceedings of the 16th British National Conferenc on Databases: Advances in Databases
WUM - A Tool for WWW Ulitization Analysis
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Discovering Trends and Relationships among Rules
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
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In the last years the datasets available have grown tremendously, and the development of efficient and scalable data mining algorithms has become a major research challenge. However, since the data is more dynamic than static there is also a strong need to update previously discovered rules and patterns. Recently, a couple of studies have emerged dealing with the topic of incremental update of discovered knowledge. These studies mostly concentrate on the question whether new rules emerge or old ones become extinct. We present a framework that enables the analyst to monitor the changes a rule may undergo when the dataset the rules were discovered from is updated, and to observe emerging trends as data change. We propose a generic rule model that distinguishes between different types of pattern changes, and provide formal definitions for these. We present our approach in a case study on the evolution of web usage patterns. These patterns have been stored in a database and are used to observe the mining sessions as snapshots across the time series of a patterns lifetime.