Open Access
Time Series Knowledge Mining
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The article was published on 2006-01-01 and is currently open access. It has received 86 citations till now. The article focuses on the topics: Data stream mining.read more
Citations
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Dissertation
Similarity search for multidimensional data sequences = 다차원 데이터 시퀀스에 대한 유사성 검색
Seok-Lyong Lee,이석룡 +1 more
TL;DR: To prune irrelevant sequences in a database, correct and efficient similarity functions are introduced and 73-94 percent of irrelevant sequences are pruned using the proposed method, resulting in 16-28 times faster response time compared with that of the sequential search.
Journal ArticleDOI
Data mining with Temporal Abstractions: learning rules from time series
TL;DR: The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other deals with the biological problem of inferring relationships between genes from DNA microarray data.
Journal ArticleDOI
Efficient mining of understandable patterns from multivariate interval time series
Fabian Mörchen,Alfred Ultsch +1 more
TL;DR: The Time Series Knowledge Representation (TSKR) is defined as a new language for expressing temporal knowledge in time interval data that has a hierarchical structure, with levels corresponding to the temporal concepts duration, coincidence, and partial order.
Journal ArticleDOI
Unsupervised pattern mining from symbolic temporal data
TL;DR: A unifying view of temporal concepts and data models is presented in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data as well as univariate and multivariate methods to aid the selection of the appropriate method for a given problem.
Proceedings ArticleDOI
Algorithms for time series knowledge mining
TL;DR: This work presents effective and efficient mining algorithms for interval patterns expressing the temporal concepts of coincidence and partial order based on itemset techniques and introduces a novel form of search space pruning that reduces the size of the mining result to ease interpretation and speed up the algorithms.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
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Neural Networks: A Comprehensive Foundation
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A tutorial on hidden Markov models and selected applications in speech recognition
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