Trace of objects to retrieve prediction patterns of activities in smart homes
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Discovering lag intervals for temporal dependencies
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Self-adaptive workload classification and forecasting for proactive resource provisioning
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Enabling the analysis of cross-cutting aspects in ad-hoc processes
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
Cross-Correlation Measure for Mining Spatio-Temporal Patterns
Journal of Database Management
Time series symbolization and search for frequent patterns
Proceedings of the Fourth Symposium on Information and Communication Technology
Web usage mining with evolutionary extraction of temporal fuzzy association rules
Knowledge-Based Systems
Frequence: interactive mining and visualization of temporal frequent event sequences
Proceedings of the 19th international conference on Intelligent User Interfaces
An automatic and self-adaptive multi-layer data fusion system for WiFi attack detection
International Journal of Internet Technology and Secured Transactions
Mining Therapeutic Patterns from Clinical Data for Juvenile Diabetes
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today. From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter.