Case-based reasoning
Time series forecasting using neural networks
APL '94 Proceedings of the international conference on APL : the language and its applications: the language and its applications
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A framework for the management of past experiences with time-extended situations
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Multidimensional access methods
ACM Computing Surveys (CSUR)
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive query processing for time-series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Global partial orders from sequential data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Principles of data mining
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Proceedings of the 4th International Colloquium on Grammatical Inference
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The Aware Home: A Living Laboratory for Ubiquitous Computing Research
CoBuild '99 Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
Efficient Similarity Determination and Case Construction Techniques for Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Representing Temporal Knowledge for Case-Based Prediction
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
COBRA: A CBR-Based Approach for Predicting Users Actions in a Web Site
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
MavHome: An Agent-Based Smart Home
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Nearest-Neighbours for Time Series
Applied Intelligence
Bilingual generation of weather forecasts in an operations environment
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Learning patterns in ambient intelligence environments: a survey
Artificial Intelligence Review
Using association rule mining to discover temporal relations of daily activities
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Discovering frequent user--environment interactions in intelligent environments
Personal and Ubiquitous Computing
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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Temporal data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing temporal data generated by smart-home environments. Temporal data mining in general fits into a two level architecture, where initially a transformation technique reduces data dimensionality in the first level and indexing techniques provide efficient access to the data in the second level. This infrastructure of temporal data mining provides the basis for high-level data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main temporal data mining techniques available and provides examples of where they can be applied within a smart home environment.