The Experts below are selected from a list of 54051 Experts worldwide ranked by ideXlab platform
John F. Roddick - One of the best experts on this subject based on the ideXlab platform.
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armada an algorithm for discovering richer relative Temporal association rules from Interval based data
Data and Knowledge Engineering, 2007Co-Authors: Edi Winarko, John F. RoddickAbstract:Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most Temporal data mining research has focused on events existing at a point in time rather than over a Temporal Interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating Temporal Intervals offers rules that are richer still. In this paper we outline a new algorithm, ARMADA, to discover frequent Temporal patterns and to generate richer Interval-based Temporal association rules. In addition, we introduce a maximum gap time constraint that can be used to get rid of insignificant patterns and rules so that the number of generated patterns and rules can be reduced. Synthetic datasets are utilized to assess the performance of the algorithm.
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Linear Temporal sequences and their interpretation using midpoint relationships
IEEE Transactions on Knowledge and Data Engineering, 2005Co-Authors: John F. Roddick, Carl MooneyAbstract:The Temporal Interval relationships formalized by Allen, and later extended to accommodate semiIntervals by Freksa, have been widely utilized in both data modeling and artificial intelligence research to facilitate reasoning between the relative Temporal ordering of events. In practice, however, some modifications to the relationships are necessary when linear Temporal sequences are provided, when event times are aggregated, or when data is supplied to a granularity which is larger than required. This paper discusses these modifications and outlines a solution to this problem which accommodates any available knowledge of Interval midpoints.
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discovering richer Temporal association rules from Interval based data
Lecture Notes in Computer Science, 2005Co-Authors: Edi Winarko, John F. RoddickAbstract:Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most Temporal data mining research has focused on events existing at a point in time rather than over a Temporal Interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating Temporal Intervals offers rules that are richer still. In this paper we outline a new algorithm to discover frequent Temporal patterns and to generate richer Interval-based Temporal association rules.
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IDEAL - Visualisation of Temporal Interval Association Rules
Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining Financial Engineering and Intelligent Agents, 2000Co-Authors: Chris P. Rainsford, John F. RoddickAbstract:Temporal Intervals and the interaction of Interval-based events are fundamental in many domains including medicine, commerce, computer security and various types of normalcy analysis. In order to learn from Temporal Interval data we have developed a Temporal Interval association rule algorithm. In this paper, we will provide a definition for Temporal Interval association rules and present our visualisation techniques for viewing them. Visualisation techniques are particularly important because the complexity and volume of knowledge that is discovered during data mining often makes it difficult to comprehend. We adopt a circular graph for visualising a set of associations that allows underlying patterns in the associations to be identified. To visualize Temporal relationships, a parallel coordinate graph for displaying the Temporal relationships has been developed.
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PRICAI - Temporal Interval logic in data mining
PRICAI 2000 Topics in Artificial Intelligence, 2000Co-Authors: Chris P. Rainsford, John F. RoddickAbstract:The last decade has seen the emergence of data mining as a significant field of research. Whilst the exploitation of time series data has been widely examined in this context, the accommodation of Temporal Interval semantics has not been widely investigated. Temporal Intervals and the interaction of Interval-based events are fundamental in many domains including commerce, medicine, computer security and various types of normalcy analysis. We have developed an algorithm for integrating Temporal Interval semantics into association rules, a form of rule that has become widely used in data mining. We have also developed a visualisation technique to view the discovered rules. The model of Temporal reasoning that has been adopted acconmiodates both point-based and Interval-based models of time simultaneously. In addition, the use of a generalized taxonomy of Temporal relationships supports the generalization of Temporal relationships and their specification at different levels of abstraction. This approach also facilitates the possibility of reasoning with incomplete or missing information.
Keun Ho Ryu - One of the best experts on this subject based on the ideXlab platform.
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mining Temporal Interval relational rules from Temporal data
Journal of Systems and Software, 2009Co-Authors: Yong Joon Lee, Jun Wook Lee, Duck Jin Chai, Bu Hyun Hwang, Keun Ho RyuAbstract:Temporal data mining is still one of important research topic since there are application areas that need knowledge from Temporal data such as sequential patterns, similar time sequences, cyclic and Temporal association rules, and so on. Although there are many studies for Temporal data mining, they do not deal with discovering knowledge from Temporal Interval data such as patient histories, purchaser histories, and web logs etc. We propose a new Temporal data mining technique that can extract Temporal Interval relation rules from Temporal Interval data by using Allen's theory: a preprocessing algorithm designed for the generalization of Temporal Interval data and a Temporal relation algorithm for mining Temporal relation rules from the generalized Temporal Interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques.
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EurAsia-ICT - Discovering Temporal Relation Rules Mining from Interval Data
Lecture Notes in Computer Science, 2002Co-Authors: Jun Wook Lee, Yong Joon Lee, Hey Kyu Kim, Bu Hun Hwang, Keun Ho RyuAbstract:In this paper, we propose a new data mining technique that can address the Temporal relation rules of Temporal Interval data by using Allen's theory. We present two new algorithms for discovering Temporal relationships: one is to preprocess an algorithm for the generalization of Temporal Interval data and to transform timestamp data into Temporal Interval data; and the other is to use a Temporal relation algorithm for mining Temporal relation rules and to discover the rules from Temporal Interval data. This technique can provide more useful knowledge in comparison with other conventional data mining techniques.
Yong Joon Lee - One of the best experts on this subject based on the ideXlab platform.
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mining Temporal Interval relational rules from Temporal data
Journal of Systems and Software, 2009Co-Authors: Yong Joon Lee, Jun Wook Lee, Duck Jin Chai, Bu Hyun Hwang, Keun Ho RyuAbstract:Temporal data mining is still one of important research topic since there are application areas that need knowledge from Temporal data such as sequential patterns, similar time sequences, cyclic and Temporal association rules, and so on. Although there are many studies for Temporal data mining, they do not deal with discovering knowledge from Temporal Interval data such as patient histories, purchaser histories, and web logs etc. We propose a new Temporal data mining technique that can extract Temporal Interval relation rules from Temporal Interval data by using Allen's theory: a preprocessing algorithm designed for the generalization of Temporal Interval data and a Temporal relation algorithm for mining Temporal relation rules from the generalized Temporal Interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques.
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EurAsia-ICT - Discovering Temporal Relation Rules Mining from Interval Data
Lecture Notes in Computer Science, 2002Co-Authors: Jun Wook Lee, Yong Joon Lee, Hey Kyu Kim, Bu Hun Hwang, Keun Ho RyuAbstract:In this paper, we propose a new data mining technique that can address the Temporal relation rules of Temporal Interval data by using Allen's theory. We present two new algorithms for discovering Temporal relationships: one is to preprocess an algorithm for the generalization of Temporal Interval data and to transform timestamp data into Temporal Interval data; and the other is to use a Temporal relation algorithm for mining Temporal relation rules and to discover the rules from Temporal Interval data. This technique can provide more useful knowledge in comparison with other conventional data mining techniques.
Dean V. Buonomano - One of the best experts on this subject based on the ideXlab platform.
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Temporal Interval Learning in Cortical Cultures Is Encoded in Intrinsic Network Dynamics.
Neuron, 2016Co-Authors: Anubhuti Goel, Dean V. BuonomanoAbstract:Summary Telling time and anticipating when external events will happen is among the most important tasks the brain performs. Yet the neural mechanisms underlying timing remain elusive. One theory proposes that timing is a general and intrinsic computation of cortical circuits. We tested this hypothesis using electrical and optogenetic stimulation to determine if brain slices could "learn" Temporal Intervals. Presentation of Intervals between 100 and 500 ms altered the Temporal profile of evoked network activity in an Interval and pathway-specific manner—suggesting that the network learned to anticipate an expected stimulus. Recordings performed during training revealed a progressive increase in evoked network activity, followed by subsequent refinement of Temporal dynamics, which was related to a time-window-specific increase in the excitatory-inhibitory balance. These results support the hypothesis that subsecond timing is an intrinsic computation and that timing emerges from network-wide, yet pathway-specific, changes in evoked neural dynamics.
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Learning and Generalization of Auditory Temporal–Interval Discrimination in Humans
The Journal of neuroscience : the official journal of the Society for Neuroscience, 1997Co-Authors: Beverly A. Wright, Dean V. Buonomano, Henry W. Mahncke, Michael M. MerzenichAbstract:The sensory encoding of the duration, Interval, and order of different stimulus features provides vital information to the nervous system. The present study focuses on the influence of practice on auditory Temporal-Interval discrimination. The goals of the experiment were to determine (1) whether practice improved the ability to discriminate a standard Interval of 100 msec bounded by brief 1 kHz tones from longer Intervals, and, if so, (2) whether this improvement generalized to different tonal frequencies or Temporal Intervals. Learning was examined in 14 human subjects using an adaptive, two-alternative, forced-choice procedure. One hour of training per day for 10 d led to marked improvements in the ability to discriminate between the standard and longer Intervals. The generalization of learning was evaluated by independently varying the spectral (tonal frequency) and Temporal (Interval) components of the stimuli in four conditions tested both before and after the training phase. Remarkably, there was complete generalization to the trained Interval of 100 msec bounded by tones at the untrained frequency of 4 kHz, but no generalization to the untrained Intervals of 50, 200, or 500 msec bounded by tones at the trained frequency of 1 kHz. Thus, these data show that (1) Temporal-Interval discrimination using a 100-msec standard undergoes perceptual learning, and (2) the neural mechanisms underlying this learning are Temporally, but not spectrally, specific. These results are compared with those from previous investigations of learning in visual spatial tasks, and are discussed in relation to biologically plausible models of Temporal processing.
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learning and generalization of auditory Temporal Interval discrimination in humans
The Journal of Neuroscience, 1997Co-Authors: Beverly A. Wright, Dean V. Buonomano, Henry W. Mahncke, Michael M. MerzenichAbstract:The sensory encoding of the duration, Interval, and order of different stimulus features provides vital information to the nervous system. The present study focuses on the influence of practice on auditory Temporal-Interval discrimination. The goals of the experiment were to determine (1) whether practice improved the ability to discriminate a standard Interval of 100 msec bounded by brief 1 kHz tones from longer Intervals, and, if so, (2) whether this improvement generalized to different tonal frequencies or Temporal Intervals. Learning was examined in 14 human subjects using an adaptive, two-alternative, forced-choice procedure. One hour of training per day for 10 d led to marked improvements in the ability to discriminate between the standard and longer Intervals. The generalization of learning was evaluated by independently varying the spectral (tonal frequency) and Temporal (Interval) components of the stimuli in four conditions tested both before and after the training phase. Remarkably, there was complete generalization to the trained Interval of 100 msec bounded by tones at the untrained frequency of 4 kHz, but no generalization to the untrained Intervals of 50, 200, or 500 msec bounded by tones at the trained frequency of 1 kHz. Thus, these data show that (1) Temporal-Interval discrimination using a 100-msec standard undergoes perceptual learning, and (2) the neural mechanisms underlying this learning are Temporally, but not spectrally, specific. These results are compared with those from previous investigations of learning in visual spatial tasks, and are discussed in relation to biologically plausible models of Temporal processing.
Jun Wook Lee - One of the best experts on this subject based on the ideXlab platform.
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mining Temporal Interval relational rules from Temporal data
Journal of Systems and Software, 2009Co-Authors: Yong Joon Lee, Jun Wook Lee, Duck Jin Chai, Bu Hyun Hwang, Keun Ho RyuAbstract:Temporal data mining is still one of important research topic since there are application areas that need knowledge from Temporal data such as sequential patterns, similar time sequences, cyclic and Temporal association rules, and so on. Although there are many studies for Temporal data mining, they do not deal with discovering knowledge from Temporal Interval data such as patient histories, purchaser histories, and web logs etc. We propose a new Temporal data mining technique that can extract Temporal Interval relation rules from Temporal Interval data by using Allen's theory: a preprocessing algorithm designed for the generalization of Temporal Interval data and a Temporal relation algorithm for mining Temporal relation rules from the generalized Temporal Interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques.
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EurAsia-ICT - Discovering Temporal Relation Rules Mining from Interval Data
Lecture Notes in Computer Science, 2002Co-Authors: Jun Wook Lee, Yong Joon Lee, Hey Kyu Kim, Bu Hun Hwang, Keun Ho RyuAbstract:In this paper, we propose a new data mining technique that can address the Temporal relation rules of Temporal Interval data by using Allen's theory. We present two new algorithms for discovering Temporal relationships: one is to preprocess an algorithm for the generalization of Temporal Interval data and to transform timestamp data into Temporal Interval data; and the other is to use a Temporal relation algorithm for mining Temporal relation rules and to discover the rules from Temporal Interval data. This technique can provide more useful knowledge in comparison with other conventional data mining techniques.
