Algorithms for time series knowledge mining

Moerchen, Fabian

doi:10.1145/1150402.1150485

Cited by 90 publications

(59 citation statements)

References 14 publications

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“…Unfortunately, Allen's relations are hampered by the fact that they cannot capture this variability and thus the representation of a relation might have more than one meaning. This issue has also been addressed in (Moerchen, 2006) and is illustrated in Figure 4. Consider, for instance, the case where the actual relation between two event intervals is M eets, but due to noise it appears as Overlaps (Figure 4 .…”

Section: Ambiguity Issuesmentioning

confidence: 87%

Mining frequent arrangements of temporal intervals

et al. 2009

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Abstract. The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.

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Section: Ambiguity Issuesmentioning

confidence: 87%

Mining frequent arrangements of temporal intervals

et al. 2009

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“…Sequence mining techniques discover item-sets where the time variable is discrete (i.e., when time is divided into intervals) [14,15]. Sequence mining techniques were used over clinical data in a variety of scenarios: recommender systems, decision support systems and theoretical research [14 -18].…”

Section: The Spade Sequence Mining Algorithmmentioning

confidence: 99%

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

Pimus¹,

Peleg

Schertz³

2016

Methods Inf Med

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SummaryObjectives: Understanding the progression of comorbid neurodevelopmental disorders (NDD) during different critical time periods may contribute to our comprehension of the underlying pathophysiology of NDDs. The objective of our study was to identify frequent temporal sequences of developmental diagnoses in noisy patient data. Methods: We used a data set of 2810 patients, documenting NDD diagnoses given to them by an NDD expert at a child developmental center during multiple visits at different ages. Extensive preprocessing steps were developed in order to allow the data set to be processed by an efficient sequence mining algorithm (SPADE). Results: The discovered sequences were validated by cross validation for 10 iterations; all correlation coefficients for support, confidence and lift measures were above 0.75 and their proportions were similar. No significant differences between the distributions of sequences were found using KolmogorovSmirnov test. Conclusions:We have demonstrated the feasibility of using the SPADE algorithm for discovery of valid temporal sequences of comorbid disorders in children with NDDs. The identification of such sequences would be beneficial from clinical and research perspectives. Moreover, these sequences could serve as features for developing a full-fledged temporal predictive model.

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“…Clustering of time series is concerned with grouping a collection of time series (or sequences) based on their similarity. Time series clustering has been shown effective in providing useful information in various domains [13]. For example, in financial data, clustering can be used to group stocks that exhibit same trends in price movements.Clustering of sequences is relatively less explored but is becoming increasingly important in data mining applications such as web usage mining and bioinformatics [5].…”

Section: All Temporal Sequential Pattern In Data Mining Tasksmentioning

confidence: 99%

“…For example, in financial data, clustering can be used to group stocks that exhibit same trends in price movements.Clustering of sequences is relatively less explored but is becoming increasingly important in data mining applications such as web usage mining and bioinformatics [5]. A survey on clustering time series has been presented by Liao (2005) [13].…”

Section: All Temporal Sequential Pattern In Data Mining Tasksmentioning

confidence: 99%

Mining Signatures from Event Sequences

S.H.¹,

Jadhav²,

Deshmukh³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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This paper proposes a novel secular knowledge representation and learning framework to proposed largescale secular signature mining of longitudinal heterogeneous occasional data. The framework allows the presentation, extra4ction, and mining of high order latent occasion event structure and relationships between single and many sequences. The prescribed data representation maps the heterogeneous sequences to a image by encoding occasions as a structured spatial-secular shape process. We have suggested clinical assessment for naked interactive knowledge discovery in large electronic health record databases.

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Algorithms for time series knowledge mining

Cited by 90 publications

References 14 publications

Mining frequent arrangements of temporal intervals

Mining frequent arrangements of temporal intervals

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

Mining Signatures from Event Sequences

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