Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Adaptive and Resource-Aware Mining of Frequent Sets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Memory-adative association rules mining
Information Systems - Databases: Creation, management and utilization
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning and inferring transportation routines
Artificial Intelligence
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
Context-aware adaptive data stream mining
Intelligent Data Analysis - Knowledge Discovery from Data Streams
A holistic approach for resource-aware adaptive data stream mining
New Generation Computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Information Sciences: an International Journal
MobiAd: private and scalable mobile advertising
Proceedings of the fifth ACM international workshop on Mobility in the evolving internet architecture
A personal route prediction system based on trajectory data mining
Information Sciences: an International Journal
Personalized recommendation of popular blog articles for mobile applications
Information Sciences: an International Journal
Nemoz: a distributed framework for collaborative media organization
Ubiquitous knowledge discovery
Quality-driven resource-adaptive data stream mining?
ACM SIGKDD Explorations Newsletter
Bayesian networks to predict data mining algorithm behavior in ubiquitous computing environments
MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
Context-Aware ubiquitous data mining based agent model for intersection safety
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
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Ubiquitous computing software needs to be autonomous so that essential decisions such as how to configure its particular execution are self-determined. Moreover, data mining serves an important role for ubiquitous computing by providing intelligence to several types of ubiquitous computing applications. Thus, automating ubiquitous data mining is also crucial. We focus on the problem of automatically configuring the execution of a ubiquitous data mining algorithm. In our solution, we generate configuration decisions in a resource aware and context aware manner since the algorithm executes in an environment in which the context often changes and computing resources are often severely limited. We propose to analyze the execution behavior of the data mining algorithm by mining its past executions. By doing so, we discover the effects of resource and context states as well as parameter settings on the data mining quality. We argue that a classification model is appropriate for predicting the behavior of an algorithm's execution and we concentrate on decision tree classifier. We also define taxonomy on data mining quality so that tradeoff between prediction accuracy and classification specificity of each behavior model that classifies by a different abstraction of quality, is scored for model selection. Behavior model constituents and class label transformations are formally defined and experimental validation of the proposed approach is also performed.