Data mining with Temporal Abstractions: learning rules from time series

Sacchi, Lucia; Larizza, Cristiana; Combi, Carlo; Bellazzi, Riccardo

doi:10.1007/s10618-007-0077-7

Cited by 120 publications

(60 citation statements)

References 37 publications

(48 reference statements)

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“…We note that possible similar results could potentially be discovered with other approaches (e.g., temporal association rules with abstractions [16,36], or sequence mining [17,37]). Some of these intervalbased methods have been specifically extended to handle classification and prediction tasks, in particular, in clinical domains [38].…”

Section: Figuresupporting

confidence: 72%

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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Section: Figuresupporting

confidence: 72%

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

Pimus¹,

Peleg

Schertz³

2016

Methods Inf Med

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“…This ontology, including the taxonomies describing temporal entities, data types, algorithms and temporal operators, is inspired by the temporal abstraction framework first presented in [4] and later formalized in [25]. With respect to this framework, two new concepts have been introduced, Aggregation and Combiner, both needed for reasoning with time intervals.…”

Section: Methodological Ontologymentioning

confidence: 99%

JTSA: An open source framework for time series abstractions

Sacchi

Capozzi²,

Bellazzi

et al. 2015

Computer Methods and Programs in Biomedicine

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“…Then, a temporal data mining process can be applied to the abstract, interval-based concepts that are the output of the TA process; these interval-based concepts and the patterns that can be formed from them may have a more significant predictive value, and can be used for various data mining and classification tasks Sacchi et al 2007;Batal et al 2009) (indicated by (c2) in Fig. 1).…”

Section: Related Workmentioning

confidence: 99%

A distributed architecture for efficient parallelization and computation of knowledge-based temporal abstractions

Shabtai¹,

Shaḥar²,

Elovici³

2011

J Intell Inf Syst

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Today, data storage capabilities as well as computational power are rapidly increasing. On the one hand, this improvement makes it possible to generate and store a great amount of temporal (time-oriented) data for future query, analysis and discovery of new knowledge. On the other hand, systems and experts are encountering new problems in processing this increased amount of data. The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of temporal data. One approach is to use an automatic summarization process based on predefined knowledge, such the Knowledge-Based Temporal-Abstraction (KBTA) method. This method enables one to summarize and reduce the amount of raw data by creating higher level interpretations based on predefined domain knowledge. Unfortunately, the task of temporal abstraction is inherently computationally expensive, especially when an enormous volume of multivariate data has to be handled and when complex patterns need to be considered. In this research, we address the scalability problem of a temporal-abstraction task that involves processing significantly large amounts of raw data. We propose a new computational framework, the Distributed KBTA (DKBTA), which efficiently distributes the abstraction process among several parallel computational nodes, in order to achieve an acceptable computation time. The DKBTA framework distributes the temporal-abstraction process along one or more computational axes, each of which enables parallelization of one or more temporalabstraction tasks into which the main temporal-abstraction task is decomposed, 250 J Intell Inf Syst (2012) 39:249-286 such as by different subject groups, concepts types, or abstraction types. We have implemented the DKBTA framework and have evaluated it in a preliminary fashion in the medical and the information security domains, with encouraging results. In our small-scale evaluation, only distribution along the subjects axis and sometimes along the concept-type axis seemed to consistently enhance performance, and only for computations involving individual subjects and not functions of sets of subjects; but this observation might depend on the number of processing units. Additionally, since the communication between the processing units was based on the TCP protocol, we could not observe any speedup even when using two processing units on the same machine. In our further evaluations we plan to use a shared memory architecture in order to exchange data between processing units.

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Data mining with Temporal Abstractions: learning rules from time series

Cited by 120 publications

References 37 publications

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

JTSA: An open source framework for time series abstractions

A distributed architecture for efficient parallelization and computation of knowledge-based temporal abstractions

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