Open AccessJournal Article
Temporal evolution and local patterns
Myra Spiliopoulou,Steffan Baron +1 more
6
TL;DR: The temporal model for patterns as evolving objects is described and a criteria to capture the interestingness of pattern change is proposed and heuristics that trace interesting changes are presented.
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Abstract: We elaborate on the subject of pattern change as a result of population evolution. We provide an overview of literature threads relevant to this subject, where the focus is on related works in the area of pattern adaptation rather than on modelling or understanding change. We then describe our temporal model for patterns as evolving objects and propose criteria to capture the interestingness of pattern change. We also present heuristics that trace interesting changes.
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Citations
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Efficient monitoring of patterns in data mining environments
TL;DR: In this paper, a general framework for monitoring patterns and detecting interesting changes without continuously mining the data is introduced, which is based on a temporal representation for patterns, in which both the content and the statistics of a pattern are modeled.
48
•Journal Article
Discovering unexpected patterns in temporal data using temporal logic
Gideon Berger,Alexander Tuzhilin +1 more
TL;DR: A probabilistic measure of interestingness based on unexpectedness is presented, whereby a pattern P is deemed interesting if the ratio of the actual number of occurrences of P exceeds the expected number of occurrence of P by some user defined threshold.
19
Decentralized online clustering for supporting autonomic management of distributed systems
Andres Quiroz Hernandez
- 01 Jan 2010
TL;DR: Decentralized Online Clustering for Supporting Autonomic Management of Distributed Systems by Andres QUIROZ HERNANDEZ Dissertation Director: Professor Manish Parashar
Discovering Trends and Relationships among Rules
Chaohai Chen,Wynne Hsu,Mong Li Lee +2 more
- 25 Aug 2009
TL;DR: This work introduces the notion of trend fragment to facilitate the analysis of relationships among rules to improve the quality of temporal association rules mining algorithms.
1
Changing User Interests through Prior-Learning of Context
Ivan Koychev
- 01 Jan 2002
Abstract: The paper presents an algorithm for learning drifting and recurring user interests. The algorithm uses a prior-learning level to find out the current context. After that, searches into past observations for episodes that are relevant to the current context, 'remembers' them and 'forgets' the irrelevant ones. Finally, the algorithm learns only from the selected relevant examples. The experiments conducted with a data set about calendar scheduling recommendations show that the presented algorithm improves significantly the predictive accuracy.
1