Discovering Productive Periodic Frequent Patterns in Transactional Databases

Nofong, Vincent Mwintieru

doi:10.1007/978-3-319-24474-7_20

Cited by 2 publications

(5 citation statements)

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“…In [29], a productive periodic pattern is generated; productive patterns are exhibited frequently, and this regularity is not due to the random occurrences of uncorrelated items. Further, the framework in [29] limits the pattern's periodicity to a given threshold and within the same range of a period's values given by the user. The model uses the basic a priori-like approach to generate the periodic pattern.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, using only the average as a sole periodicity measure is misleading and will not solve the problem of reporting patterns with similar periods. The work in [29] solved this issue by combining the average periodicity measure with the standard deviation measures(s). Here, we use the same solution, but we restrict our periodicity only to the interested periods that match the user's request.…”

Section: Are Not Periodic-recurrence As Their Value ≰ ( )mentioning

confidence: 99%

“…Moreover, how we can detect the relationships between different BSN reading parameters and ensure that the generated patterns are not due to random occurrences is a major issue in analyzing the vital signs obtained from BSNs. To enable reporting only the periodic-frequent physiological parameters that are vital for decision making, we test the positive correlations among them using the productive-associated test as proposed in [29] as follows.…”

Section: Definition 5 (Problem Definition)mentioning

confidence: 99%

“…-PPFP is our implementation of the work in [29] that reports productive periodic-frequent patterns using an a priori-like approach [40].…”

Section: Experimental Studymentioning

confidence: 99%

“…The work of [29] introduced a new type of periodic pattern named productive patterns, which have the ability to find full period patterns (i.e., patterns that occur at regular intervals), and conducted a productivity test to ensure the association between the generated patterns. Nevertheless, the approach uses a generate-and-test method based on the a priori algorithm, which results in huge search space and thus cannot be applied in the case of stream data [21].…”

mentioning

confidence: 99%

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Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Ismail¹,

Hassan²

2017

Preprint

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Abstract:The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productiveassociated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in a smart home. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.Keywords: Body sensor network; Smart home, knowledge discovery in BSN data; frequent patterns; periodic patterns and productive pattern.

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Section: Related Workmentioning

confidence: 99%

Section: Are Not Periodic-recurrence As Their Value ≰ ( )mentioning

confidence: 99%

Section: Definition 5 (Problem Definition)mentioning

confidence: 99%

“…-PPFP is our implementation of the work in [29] that reports productive periodic-frequent patterns using an a priori-like approach [40].…”

Section: Experimental Studymentioning

confidence: 99%

mentioning

confidence: 99%