Efficient Mining Regularly Frequent Patterns in Transactional Databases

Rashid, Md. Mamunur; Karim, Md. Rezaul; Jeong, Byeongmoon; Choi, Ho-Jin

doi:10.1007/978-3-642-29038-1_20

Cited by 37 publications

(20 citation statements)

References 12 publications

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“…We assumed the missed readings from sensors as undetected events which are beneficial in generating patterns despite lost readings. In absence of any regularly frequent sensor pattern mining technique in literature for sensor dataset, we compared the performance of RFSP-tree with RF-Tree [23], which was proposed to mine regularly frequent patterns in transactional database and shown to outperform other related techniques.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Rashid et al [23] have introduced a method of finding regularly frequent patterns in transactional databases that follow a temporal regularity in their occurrence characteristics by using a tree structure, called a Regularly Frequent Pattern tree (RF-tree). However, the requirement of two database scans for RF-tree is inefficient in mining regularly frequent sensor patterns from sensor datasets.…”

Section: Related Workmentioning

confidence: 99%

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Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

Rashid

Gondal

Kamruzzaman

2017

Information Sciences

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

Rashid

Gondal

Kamruzzaman

2017

Information Sciences

Self Cite

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“…For instance, the periodicity measure in [27], which is susceptible to noise in the database, might often report the noised maximal period of a pattern as its regular period. Additionally, as we mentioned earlier, the methods in [25] and [26] often generate regular (periodic) frequent patterns that occur in the whole database with totally distinct periods.…”

Section: Related Workmentioning

confidence: 99%

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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“…An-other calculation has been proposed RFRP to locate the general successive examples of a thing set. In this paper [15] event recurrence of the de-sign is estimated as a critical factor and is utilized for estimating the intriguing quality of the example in numerous applications. Considering min support client given edge esteem a consistent continuous example is found.…”

Section: Introductionmentioning

confidence: 99%

Regular frequent crime pattern mining on crime datasets

Kumar¹,

Sreedevi²,

Krishna³

et al. 2018

IJET

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The objective of violation information mining is to comprehend different violation designs in criminal conduct in request to foresee viola-tions and expect criminal movement to stay away from the violation not to happen. Foreseeing violation is one of the worldwide difficulties looking by Law authorization office and it requires tireless endeavors with a specific end goal to limit. In this paper we are presenting anoth-er violation design called general incessant violation design which happens frequently at certain time interims utilizing vertical information arrange additionally fulfills descending conclusion property. Violation designs were not characterized by insights and its distinguishing proof is some-thing other than checking and abridging violations that are comparable in attributes and additionally area on a guide. Violation design is a gathering of at least one violations answered to or on the other hand found by the police.

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Efficient Mining Regularly Frequent Patterns in Transactional Databases

Cited by 37 publications

References 12 publications

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

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

Regular frequent crime pattern mining on crime datasets

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