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182
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
 In Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
, 2002
"... We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede t ..."
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Cited by 31 (3 self)
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We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede the occurrence of patterns in other sequences, with respect to userspecified constraints. In addition to the traditional frequency and support constraints in sequential data mining, this approach uses separate antecedent and consequent inclusion constraints.
TimeSeries Novelty Detection Using OneClass Support Vector
 Machines,” Proc. Int’l Joint Conf. Neural Networks (IJCNN ’03
, 2003
"... Abstract Timeseries novelty detection, or anomaly detection, refers to the automatic identification of novel or abnormal events embedded in normal timeseries points. Although it is a challenging topic in data mining, it has been acquiring increasing attention due to its huge potential for immedia ..."
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Cited by 28 (0 self)
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Abstract Timeseries novelty detection, or anomaly detection, refers to the automatic identification of novel or abnormal events embedded in normal timeseries points. Although it is a challenging topic in data mining, it has been acquiring increasing attention due to its huge potential for immediate applications. I n this paper, a new algorithm for timeseries novelty detection based on oneclass support vector machines (SVMs) is proposed. The concepts o f phase and projected phase spaces are first introduced, which allows us to convert a timeseries into a set of vectors in the (projected) phase spaces. Then we interpret novel events in timeseries as outliers of the “normal ” distribution of the converted vectors in the (projected) phase spaces. Oneclass SVMs are employed as the outlier detectors. I n order to obtain robust detection results, a technique to combine intermediate results at different phase spaces is also proposed, Experiments on hotb synthetic and measured data are presented to demonstrate the promising performance of the new algorithm. I.
Generating Rule Sets from Model Trees
 in Proc. of the 12th Australian Joint Conf. on Artificial Intelligence
"... Abstract. Model trees—decision trees with linear models at the leaf nodes—have recently emerged as an accurate method for numeric prediction that produces understandable models. However, it is known that decision lists—ordered sets of IfThen rules—have the potential to be more compact and therefore ..."
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Cited by 25 (0 self)
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Abstract. Model trees—decision trees with linear models at the leaf nodes—have recently emerged as an accurate method for numeric prediction that produces understandable models. However, it is known that decision lists—ordered sets of IfThen rules—have the potential to be more compact and therefore more understandable than their tree counterparts. We present an algorithm for inducing simple, accurate decision lists from model trees. Model trees are built repeatedly and the best rule is selected at each iteration. This method produces rule sets that are as accurate but smaller than the model tree constructed from the entire dataset. Experimental results for various heuristics which attempt to find a compromise between rule accuracy and rule coverage are reported. We show that our method produces comparably accurate and smaller rule sets than the commercial stateoftheart rule learning system Cubist. 1
Learning to predict the effects of actions: Synergy between rules and landmarks
 In Proceedings of the 6th (IEEE) International Conference on Development and Learning
, 2007
"... Abstract — A developing agent must learn the structure of its world, beginning with its sensorimotor world. It learns rules to predict how its motor signals change the sensory input it receives. It learns the limits to its motion. It learns which effects of its actions are unconditional and which ef ..."
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Cited by 25 (20 self)
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Abstract — A developing agent must learn the structure of its world, beginning with its sensorimotor world. It learns rules to predict how its motor signals change the sensory input it receives. It learns the limits to its motion. It learns which effects of its actions are unconditional and which effects are conditional, including what they depend on. We present preliminary results evaluating an implemented computational model of this important kind of foundational developmental learning. Our model demonstrates synergy between the learning of landmarks representing important qualitative distinctions, and the learning of rules that exploit those distinctions to make reliable predictions. These qualitative distinctions make it possible to define discrete events, and then to identify predictive rules describing regularities among events and the values of context variables. The attention of the learning agent is focused by a stratified model that structures the set of variables, and the structure of the stratified model is simultaneously created by the learning process. Index Terms — developmental learning, sensorimotor learning, qualitative abstraction, predictive rules, landmark values I.
A WaveletBased Anytime Algorithm for KMeans Clustering of Time Series
 In Proc. Workshop on Clustering High Dimensionality Data and Its Applications
, 2003
"... The emergence of the field of data mining in the last decade has sparked an increasing interest in clustering of tiate series. Although there has been much research on clustering in general, most classic machine learning and data mining algorithms do not work well for time series due to their unique ..."
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Cited by 25 (3 self)
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The emergence of the field of data mining in the last decade has sparked an increasing interest in clustering of tiate series. Although there has been much research on clustering in general, most classic machine learning and data mining algorithms do not work well for time series due to their unique structure. In particular, the high dimensionaliF, very high feature correlation, and the (typically) large amount of noise that characterize time series data present a difficult challenge. In this work we address these challenges by introducing a novel anytiate version of kMeans clustering algorithm for time series. The algorithm works by leveraging off the multiresolution property of wavelets. In particular, an initial clustering is perforated with a very coarse resolution representation of the data. The results obtained from this "quick and dirty" clustering are used to initialize a clustering at a slightly finer level of approximation. This process is repeated until the clustering results stabilize or until the "approxiatation" is the raw data. In addition to casting kMeans as an anytime algorithm, our approach has two other very unintuitive properties. The quality of the clustering is often better than the batch algorithm, and even if the algorithm is run to coatpletion, the time taken is typically much less than the time taken by the original algorithm. We explain, and eatpirically demonstrate these surprising and desirable properties with coatprehensive experiatents on several publicly available real data sets.
Mining Mobile Sequential Patterns in a Mobile Commerce Environment
, 2007
"... In this paper, we explore a new data mining capability for a mobile commerce environment. To better reflect the customer usage patterns in the mobile commerce environment, we propose an innovative mining model, called mining mobile sequential patterns, which takes both the moving patterns and purch ..."
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Cited by 24 (0 self)
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In this paper, we explore a new data mining capability for a mobile commerce environment. To better reflect the customer usage patterns in the mobile commerce environment, we propose an innovative mining model, called mining mobile sequential patterns, which takes both the moving patterns and purchase patterns of customers into consideration. How to strike a compromise among the use of various knowledge to solve the mining on mobile sequential patterns is a challenging issue. We devise three algorithms (algorithm TJLS, algorithm TJPT, and algorithm TJPF) for determining the frequent sequential patterns, which are termed large sequential patterns in this paper, from the mobile transaction sequences. Algorithm TJLS is devised in light of the concept of association rules and is used as the basic scheme. Algorithm TJPT is devised by taking both the concepts of association rules and path traversal patterns into consideration and gains performance improvement by path trimming. Algorithm TJPF is devised by utilizing the pattern family technique which is developed to exploit the relationship between moving and purchase behaviors, and thus is able to generate the large sequential patterns very efficiently. A simulation model for the mobile commerce environment is developed, and a synthetic workload is generated for performance studies. In mining mobile sequential patterns, it is shown by our experimental results that algorithm TJPF significantly outperforms others in both execution efficiency and memory saving, indicating the usefulness of the pattern family technique devised in this paper. It is shown by our results that by taking both moving and purchase patterns into consideration, one can have a better model for a mobile commerce system and is thus able to exploit the intrinsic relationship between these two important factors for the efficient mining of mobile sequential patterns.
Knowledge Discovery from Sequential Data
, 2003
"... A new framework for analyzing sequential or temporal data such as time series is proposed. It differs from other approaches by the special emphasis on the interpretability of the results, since interpretability is of vital importance for knowledge discovery, that is, the development of new knowl ..."
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Cited by 22 (0 self)
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A new framework for analyzing sequential or temporal data such as time series is proposed. It differs from other approaches by the special emphasis on the interpretability of the results, since interpretability is of vital importance for knowledge discovery, that is, the development of new knowledge (in the head of a human) from a list of discovered patterns. While traditional approaches try to model and predict all time series observations, the focus in this work is on modelling local dependencies in multivariate time series. This
A Rule Discovery Support System for Sequential Medical Data
 In the Case Study of a Chronic Hepatitis Dataset –. Int’l Workshop on Active Mining, IEEE Int’l Conf. on Data Mining (ICDM’02
, 2002
"... Abstract. It is needed for evidencebased medicine to support a medical expert in discovering clinically useful knowledge with data mining techniques. However, the real data on medical test results are severely intractable since they are sequential, largescale, and illdefined with many attributes ..."
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Cited by 21 (2 self)
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Abstract. It is needed for evidencebased medicine to support a medical expert in discovering clinically useful knowledge with data mining techniques. However, the real data on medical test results are severely intractable since they are sequential, largescale, and illdefined with many attributes and missing values. This paper discusses how preprocessing should be going and how a rule discovery support system should be developed and actually discovers medically interesting rules from the dataset on chronic hepatitis diagnosis. We have done the following preprocessing: unifying different names to the same entities, unifying different inspection cycle, discretizing timeseries, and so on. Taking a general framework of timeseries data mining based on pattern extraction and decision tree, we have discovered the rules consisted of the combination of medical test result patterns. The system has discovered medically interesting rules, and a medical expert has polished up the knowledge inspired by them through the iteration of rule discovery and evaluation. 1
Knowledge discovery from heterogeneous dynamic systems using changepoint correlations
 In ”Proceedings of the 2005 SIAM International Data Mining Conference
, 2005
"... Most of the stream mining techniques presented so far have primary paid attention to discovering association rules by direct comparison between timeseries data sets. However, their utility is very limited for heterogeneous systems, where time series of various types (discrete, continuous, oscillato ..."
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Cited by 20 (1 self)
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Most of the stream mining techniques presented so far have primary paid attention to discovering association rules by direct comparison between timeseries data sets. However, their utility is very limited for heterogeneous systems, where time series of various types (discrete, continuous, oscillatory, noisy, etc.) act dynamically in a strongly correlated manner. In this paper, we introduce a new nonlinear transformation, singular spectrum transformation (SST), to address the problem of knowledge discovery of causal relationships from a set of time series. SST is a transformation that transforms a time series into the probability density function that represents a chance to observe some particular change. For an automobile data set, we demonstrate that SST enables us to discover a hidden and useful dependency between variables.
Optimal multiscale patterns in time series streams
 In SIGMOD
, 2006
"... We introduce a method to discover optimal local patterns, which concisely describe the main trends in a time series. Our approach examines the time series at multiple time scales (i.e., window sizes) and efficiently discovers the key patterns in each. We also introduce a criterion to select the best ..."
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Cited by 19 (2 self)
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We introduce a method to discover optimal local patterns, which concisely describe the main trends in a time series. Our approach examines the time series at multiple time scales (i.e., window sizes) and efficiently discovers the key patterns in each. We also introduce a criterion to select the best window sizes, which most concisely capture the key oscillatory as well as aperiodic trends. Our key insight lies in learning an optimal orthonormal transform from the data itself, as opposed to using a predetermined basis or approximating function (such as piecewise constant, shortwindow Fourier or wavelets), which essentially restricts us to a particular family of trends. Our method lifts that limitation, while lending itself to fast, incremental estimation in a streaming setting. Experimental evaluation shows that our method can capture meaningful patterns in a variety of settings. Our streaming approach requires order of magnitude less time and space, while still producing concise and informative patterns. 1.