Mining temporal interval relational rules from temporal data

Lee, Yong Joon; Lee, Jun Wook; Chai, Duck Jin; Hwang, Bu Hyun; Ryu, Keun Ho

doi:10.1016/j.jss.2008.07.037

Cited by 39 publications

(16 citation statements)

References 18 publications

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“…Previous work studying temporal data mining has mostly focused on discovering frequent temporal patterns [4,6,15,43,33,22,2,36,46] and computing temporal abstractions [30,41,5] of time-oriented data.…”

Section: Temporal Data Miningmentioning

confidence: 99%

See 1 more Smart Citation

Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

Malik

Monroe

et al. 2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

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Finding the differences and similarities between two datasets is a common analytics task. With temporal event sequence data, this task is complex because of the many ways single events and event sequences can differ between the two datasets (or cohorts) of records: the structure of the event sequences (e.g., event order, co-occurring events, or event frequencies), the attributes of events and records (e.g., patient gender), or metrics about the timestamps themselves (e.g., event duration). In exploratory analyses, running statistical tests to cover all cases is time-consuming and determining which results are significant becomes cumbersome. Current analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. This paper presents a taxonomy of metrics for comparing cohorts of temporal event sequences, showing that the problem-space is bounded. We also present a visual analytics tool, CoCo (for "Cohort Comparison"), which implements balanced integration of automated statistics with an intelligent user interface to guide users to significant, distinguishing features between the cohorts. Lastly, we describe two early case studies: the first with a research team studying medical team performance in the emergency department and the second with pharmacy researchers.

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Section: Temporal Data Miningmentioning

confidence: 99%

“…Much of the literature is concerned with developing efficient algorithms to automatically discover frequent temporal patterns and extract temporal association rules [4,6,15,33,22,2,36]. To constrain the search procedure, some algorithms [4,15] allow users to provide initial knowledge and rules.…”

Section: Temporal Data Miningmentioning

confidence: 99%

Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

Malik

Monroe

et al. 2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

show abstract

“…Allen's theory [13]: is an algorithm that can extract temporal relations from generalized data with time intervals. the basic idea of algorithm can be summarized as follows: First, it extract the list of frequent events by calculating their supports and uniformity.…”

Section: Extract Temporal Interval Relation Rules Based Onmentioning

confidence: 99%

“…Then, frequent and uniform events are generalized and stored in a global table with these events with their time intervals restricting the first and last appearance of these events for each patient for example. To extract the temporal association rules from the generalized frequent and uniform data, we will look for all temporal relations between the events based on Allen's interval algebra [13]. Finally, extract the frequent relationship based on minimal support to define then discovering rules between frequent temporal relations.…”

Section: Extract Temporal Interval Relation Rules Based Onmentioning

confidence: 99%

Clinical Dynamic Decision Support System based on temporal association rules

Kammoun

Ayed

2014

2nd Middle East Conference on Biomedical Engineering

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Nosocomial Infections (NI) have been the major causes of morbidity and mortality of patients in intensive care units (ICUs) particularly in developing countries. Intensive surveillance and preventive measures is an effective element to fight against NI. Based on the temporal data recorded daily in the intensive care unit (ICU) and the help of some physicians, we plan to develop a Clinical Dynamic Decision Support System (CDDSS) based on knowledge discovery in databases (KDD) to help Physicians to predict and prevent NI. The CDDSS aims to the daily estimation of the NI occurrence probability, in the ICU patient hospitalization. The goal is to be able to anticipate if the association of some factors will support the appearance of the infections on the basis of patient histories. We propose to develop an algorithm for mining temporal association rules to extract temporal information. The discovery of temporal pattern would help them to take measures at time.

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“…However, for rating the customer preference of product changes over time, temporal characteristics are to be considered when a market basket analysis is applied. Data mining techniques using temporal characteristics are divided into periodic (or cyclic) pattern [9]- [11] and temporal relation [12] analyses. A periodic analysis for time-interval data discovers the cyclic pattern for repeated events during a time granularity.…”

Section: Introductionmentioning

confidence: 99%

Subspace Projection–Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service

Lee¹,

Choi²,

Jung³

et al. 2015

ETRI J

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A reliable analysis of consumer preference from a large amount of purchase data acquired in real time and an accurate customer characterization technique are essential for successful direct marketing campaigns. In this study, an optimal segmentation of post office customers in Korea is performed using a subspace projection–based clustering method to generate an accurate customer characterization from a high‐dimensional census dataset. Moreover, a traditional temporal mining method is extended to an algorithm using the MapReduce framework for a consumer preference analysis. The experimental results show that it is possible to use parallel mining through a MapReduce‐based algorithm and that the execution time of the algorithm is faster than that of a traditional method.

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Mining temporal interval relational rules from temporal data

Cited by 39 publications

References 18 publications

Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

Clinical Dynamic Decision Support System based on temporal association rules

Subspace Projection–Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service

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