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23
A better tool than Allen’s relations for expressing temporal knowledge in interval data
, 2006
"... Temporal patterns composed of symbolic intervals are commonly formulated with Allen’s interval relations originating in temporal reasoning. We show that this representation has severe disadvantages for knowledge discovery. The patterns are not robust, in the sense that small disturbances of interva ..."
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Cited by 12 (1 self)
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Temporal patterns composed of symbolic intervals are commonly formulated with Allen’s interval relations originating in temporal reasoning. We show that this representation has severe disadvantages for knowledge discovery. The patterns are not robust, in the sense that small disturbances of interval boundaries lead to different patterns for similar situations. The representation is ambiguous since the same pattern can have quantitatively widely varying appearances. For all but very simple cases the patterns are not understandable because the textual descriptions are lengthy and unstructured. We present the Time Series Knowledge Representation (TSKR), a new hierarchical language for interval patterns to express the temporal concepts of coincidence and partial order. We demonstrate the superiority of this novel form of representing temporal knowledge over Allen’s relations for data mining. Results on a real data set support our claims and show a successful application.
Indexing of Compressed Time Series
 Data Mining in Time Series Databases
"... We describe a procedure for identifying major minima and maxima of a time series, and present two applications of this procedure. The first application is fast compression of a series, by selecting major extrema and discarding the other points. The compression algorithm runs in linear time and takes ..."
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Cited by 11 (3 self)
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We describe a procedure for identifying major minima and maxima of a time series, and present two applications of this procedure. The first application is fast compression of a series, by selecting major extrema and discarding the other points. The compression algorithm runs in linear time and takes constant memory. The second application is indexing of compressed series by their major extrema, and retrieval of series similar to a given pattern. The retrieval procedure searches for the series whose compressed representation is similar to the compressed pattern. It allows the user to control the tradeoff between the speed and accuracy of retrieval. We show the effectiveness of the compression and retrieval for stock charts, meteorological data, and electroencephalograms. Keywords. Time series, compression, fast retrieval, similarity measures. 1
Extracting interpretable muscle activation patterns with time series knowledge mining
 International Journal of KnowledgeBased & Intelligent Engineering Systems
, 2005
"... The understanding of complex muscle coordination is an important goal in human movement science. There are numerous applications in medicine, sports, and robotics. The coordination process can be studied by observing complex, often cyclic movements, which are dynamically repeated in an almost ident ..."
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Cited by 10 (5 self)
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The understanding of complex muscle coordination is an important goal in human movement science. There are numerous applications in medicine, sports, and robotics. The coordination process can be studied by observing complex, often cyclic movements, which are dynamically repeated in an almost identical manner. The muscle activation is measured using kinesiological EMG. Mining the EMG data to identify patterns, which explain the interplay and coordination of muscles is a very difficult Knowledge Discovery task. We present the Time Series Knowledge Mining framework to discover knowledge in multivariate time series and show how it can be used to extract such temporal patterns.
Algorithms For Time Series Knowledge Mining
, 2006
"... Temporal patterns composed of symbolic intervals are commonly formulated with Allen’s interval relations originating in temporal reasoning. This representation has severe disadvantages for knowledge discovery. The Time Series Knowledge Representation (TSKR) is a new hierarchical language for interva ..."
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Cited by 10 (2 self)
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Temporal patterns composed of symbolic intervals are commonly formulated with Allen’s interval relations originating in temporal reasoning. This representation has severe disadvantages for knowledge discovery. The Time Series Knowledge Representation (TSKR) is a new hierarchical language for interval patterns expressing the temporal concepts of coincidence and partial order. We present effective and efficient mining algorithms for such patterns based on itemset techniques. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. On a real data set a concise set of TSKR patterns can explain the underlying temporal phenomena, whereas the patterns found with Allen’s relations are far more numerous yet only explain fragments of the data.
Discovering System Health Anomalies Using Data Mining Techniques
 Proceedings of the Joint Army Navy NASA Air Force Conference on Propulsion
, 2005
"... We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the ..."
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Cited by 9 (1 self)
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We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and costefficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in highdimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.
A Compact and Accurate Model for Classification
 IEEE Transactions on Knowledge and Data Engineering
, 2004
"... We describe and evaluate an informationtheoretic algorithm for datadriven induction of classification models based on a minimal subset of available features. The relationship between input (predictive) features and the target (classification) attribute is modeled by a treelike structure termed an ..."
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Cited by 8 (5 self)
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We describe and evaluate an informationtheoretic algorithm for datadriven induction of classification models based on a minimal subset of available features. The relationship between input (predictive) features and the target (classification) attribute is modeled by a treelike structure termed an information network (IN). Unlike other decisiontree models, the information network uses the same input attribute across the nodes of a given layer (level). The input attributes are selected incrementally by the algorithm to maximize a global decrease in the conditional entropy of the target attribute. We are using the prepruning approach: when no attribute causes a statistically significant decrease in the entropy, the network construction is stopped. The algorithm is shown empirically to produce much more compact models than other methods of decisiontree learning, while preserving nearly the same level of classification accuracy.
Unsupervised Temporal Rule Mining with Genetic Programming and Specialized Hardware
, 2003
"... Rule mining is the practice of discovering interesting and unexpected rules from large data sets. Depending on the exact problem formulation, this may be a very complicated problem. Existing methods typically make strong simplifying assumptions about the form of the rules, and limit the measure of r ..."
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Cited by 7 (1 self)
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Rule mining is the practice of discovering interesting and unexpected rules from large data sets. Depending on the exact problem formulation, this may be a very complicated problem. Existing methods typically make strong simplifying assumptions about the form of the rules, and limit the measure of rule quality to simple properties, such as confidence. Because confidence in itself is not a good indicator of how interesting a rule is to the user, the mined rules are typically sorted according to some secondary interestingness measure. In this paper we present a rule mining method that is based on genetic programming. Because we use specialized pattern matching hardware to evaluate each rule, our method supports a very wide range of rule formats, and can use any reasonable fitness measure. We develop a fitness measure that is wellsuited for our method, and give empirical results of applying the method to synthetic and realworld data sets.
Mining hierarchical temporal patterns in multivariate time series
 Proceedings of the 27th Annual German Conference in Artificial Intelligence (KI’04
, 2004
"... Abstract. The Unificationbased Temporal Grammar is a temporal extension of static unificationbased grammars. It defines a hierarchical temporal rule language to express complex patterns present in multivariate time series. The Temporal Data Mining Method is the accompanying framework to discover t ..."
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Cited by 7 (2 self)
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Abstract. The Unificationbased Temporal Grammar is a temporal extension of static unificationbased grammars. It defines a hierarchical temporal rule language to express complex patterns present in multivariate time series. The Temporal Data Mining Method is the accompanying framework to discover temporal knowledge based on this rule language. A semiotic hierarchy of temporal patterns, which are not a priori given, is build in a bottom up manner from static logical descriptions of multivariate time instants. We demonstrate the methods using music data, extracting typical parts of songs. 1
Fuzzy Clustering Based Segmentation of TimeSeries
 Lecture Notes in Computer Science
, 2003
"... The segmentation of timeseries is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a timeseries are usually vague and do not focus ..."
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Cited by 6 (1 self)
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The segmentation of timeseries is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a timeseries are usually vague and do not focused on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to group overlapping and vague objects, they cannot be directly applied to timeseries segmentation. This paper proposes a clustering algorithm for the simultaneous identification of fuzzy sets which represent the segments in time and the local PCA models used to measure the homogeneity of the segments. The algorithm is applied to the monitoring of the production of highdensity polyethylene.