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101
Discovery of Temporal Patterns  Learning Rules about the Qualitative Behaviour of Time Series
, 2001
"... . Recently, association rule mining has been generalized to the discovery of episodes in event sequences. In this paper, we additionally take durations into account and thus present a generalization to time intervals. We discover frequent temporal patterns in a single series of such labeled inte ..."
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Cited by 49 (2 self)
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. Recently, association rule mining has been generalized to the discovery of episodes in event sequences. In this paper, we additionally take durations into account and thus present a generalization to time intervals. We discover frequent temporal patterns in a single series of such labeled intervals, which we call a state sequence. A temporal pattern is dened as a set of states together with their interval relationships described in terms of Allen's interval logic, for instance \A before B, A overlaps C, C overlaps B" or equivalently \state A ends before state B starts, the gap is covered by state C". As an example we consider the problem of deriving local weather forecasting rules that allow us to conclude from the qualitative behaviour of the airpressure curve to the windstrength. Here, the states have been extracted automatically from (multivariate) time series and characterize the trend of the time series locally within the assigned time interval. 1
Knowledge discovery and interestingness measures: A survey
, 1999
"... Knowledge discovery in databases, also known as data mining, is the efficient discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analy ..."
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Cited by 48 (1 self)
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Knowledge discovery in databases, also known as data mining, is the efficient discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. An important problem in the area of data mining is the development of effective measures of interestingness for ranking the discovered knowledge. In this report, we provide a general overview of the more successful and widely known data mining techniques and algorithms, and survey seventeen interestingness measures from the literature that have been successfully employed in data mining applications. 1 1
Learning Comprehensible Descriptions of Multivariate Time Series
 In Ivan Bratko and Saso Dzeroski, editors, Proceedings of the 16 th International Conference of Machine Learning (ICML99
, 1999
"... Supervised classification is one of the most active areas of machine learning research. Most work has focused on classification in static domains, where an instantaneous snapshot of attributes is meaningful. In many domains, attributes are not static; in fact, it is the way they vary temporally that ..."
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Cited by 48 (0 self)
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Supervised classification is one of the most active areas of machine learning research. Most work has focused on classification in static domains, where an instantaneous snapshot of attributes is meaningful. In many domains, attributes are not static; in fact, it is the way they vary temporally that can make classification possible. Examples of such domains include speech recognition, gesture recognition and electrocardiograph classification. While it is possible to use ad hoc, domainspecific techniques for "flattening " the time series to a learnerfriendly representation, this fails to take into account both the special problems and special heuristics applicable to temporal data and often results in unreadable concept descriptions. Though traditional time series techniques can sometimes produce accurate classifiers, few can provide comprehensible descriptions. We propose a general architecture for classification and description of multivariate time series. It employs event primitive...
A regressionbased temporal pattern mining scheme for data streams
 In VLDB
, 2003
"... We devise in this paper a regressionbased algorithm, called algorithm FTPDS (Frequent Temporal Patterns of Data Streams), to mine frequent temporal patterns for data streams. While providing a general framework of pattern frequency counting, algorithm FTPDS has two major features, namely one data ..."
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Cited by 44 (6 self)
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We devise in this paper a regressionbased algorithm, called algorithm FTPDS (Frequent Temporal Patterns of Data Streams), to mine frequent temporal patterns for data streams. While providing a general framework of pattern frequency counting, algorithm FTPDS has two major features, namely one data scan for online statistics collection and regressionbased compact pattern representation. To attain the feature of one data scan, the data segmentation and the pattern growth scenarios are explored for the frequency counting purpose. Algorithm FTPDS scans online transaction flows and generates candidate frequent patterns in real time. The second important feature of algorithm FTPDS is on the regressionbased compact pattern representation. Specifically, to meet the space constraint, we devise for pattern representation a compact ATF (standing for Accumulated Time and Frequency) form to aggregately comprise all the information required for regression analysis. In addition, we develop the techniques of the segmentation tuning and segment relaxation to enhance the functions of FTPDS. With these features, algorithm FTPDS is able to not only conduct mining with variable time intervals but also perform trend detection effectively. Synthetic data and a real dataset which contains net
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases
 IN 4TH PACIFICASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD
, 2000
"... We address the problem of similarity search in large time series databases. We introduce ..."
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Cited by 44 (4 self)
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We address the problem of similarity search in large time series databases. We introduce
Identifying Distinctive Subsequences in Multivariate Time Series by Clustering
 PROC. ACM SIGKDD
, 1999
"... Most time series comparison algorithms attempt to discover what the members of a set of time series have in common. We investigate a different problem, determining what distinguishes time series in that set from other time series obtained from the same source. In both cases the goal is to identif ..."
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Cited by 36 (2 self)
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Most time series comparison algorithms attempt to discover what the members of a set of time series have in common. We investigate a different problem, determining what distinguishes time series in that set from other time series obtained from the same source. In both cases the goal is to identify shared patterns, though in the latter case those patterns must be distinctiveaswell. An efficient incremental algorithm for identifying distinctive subsequences in multivariate, realvalued time series is described and evaluated with data from two very different sources: the response of a set of bandpass filters to human speech and the sensors of a mobile robot.
Mining Motifs in Massive Time Series Databases
 In Proceedings of IEEE International Conference on Data Mining (ICDM’02
, 2002
"... The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously ..."
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Cited by 30 (0 self)
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The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification.
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 25 (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.
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 19 (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.