Discovering Productive Periodic Frequent Patterns in Transactional Databases

Nofong, Vincent Mwintieru

doi:10.1007/s40745-016-0078-8

Cited by 18 publications

(18 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This concept of discovering regular frequent patterns based on the proposition in Rashid et al (2012) has been used in discovering regular frequent patterns in works such as Kumar and Valli-Kumari (2013) and Rashid et al (2013). Nofong (2016) however argued that, though the proposition in Rashid et al (2012) will not miss interesting periodic frequent patterns as in Tanbeer et al (2009), algorithms that mine periodic frequent patterns using the propositions in both Tanbeer et al (2009) and Rashid et al (2012) will always report periodic frequent patterns having totally distinct periods. To report only periodic frequent patterns with similar periods for decisionmaking, Nofong (2016) defined a periodic frequent pattern as follows.…”

Section: Related Workmentioning

confidence: 99%

“…Definition 2.3 (Nofong, 2016): Given a database D, minimum support threshold ε, periodicity threshold p, difference factor p 1 , a pattern S and P S , S is a periodic frequent pattern if sup D (S) ≥ 1, (p − p 1 ) ≤ Prd(S) − std(P S ) and Prd(S) + std(P S ) ≤ (p + p 1 ).…”

Section: Related Workmentioning

confidence: 99%

“…With Definition 2.3, though PFPs having similar periodic shapes will be mined and reported, Nofong (2016) observed that some of the reported periodic frequent patterns may be periodic due to random chance without inherent item relationship. To ensure only periodic frequent patterns having inherent item relationships are mined and returned, the productiveness measure (proposed in Webb, 2010) was incorporated by Nofong (2016) in defining the productive periodic frequent patterns as the set of periodic frequent patterns with inherent item relationship.…”

Section: Related Workmentioning

confidence: 99%

“…Periodic frequent pattern (PFP) mining from transactional datasets has been widely researched on in works such as: Fournier-Viger et al 2017, Kiran and Reddy (2010), Kiran and Kitsuregawa (2013), Nofong (2016), Surana, Kiran, and Reddy (2012) and Tanbeer, Ahmed, Jeong, and Lee (2009). Several algorithms have been proposed for discovering periodic frequent patterns in transactional databases.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards fast and memory efficient discovery of periodic frequent patterns

Nofong

Wondoh

2019

Journal of Information and Telecommunication

Self Cite

View full text Add to dashboard Cite

Periodic frequent pattern (PFP) mining, the process of discovering frequent patterns that occur at regular periods in databases, is an important data mining task for various decision-making. Although several algorithms have been proposed for discovering PFPs, most of these algorithms often employ a two-stage approach to mining these periodic frequent patterns. That is, by firstly deriving the set of periods of a pattern from its coverset and subsequently evaluating the patterns' periodicity from the derived set of periods. This two-stage approach in discovering periodic frequent patterns as a result make existing algorithms inefficient in both runtime and memory usage. This paper presents solutions towards reducing the runtime, as well as, memory usage in discovering periodic frequent patterns. This is achieved by evaluating the periodicity of patterns without deriving the set of periods from their coversets. Experimental analysis on benchmark datasets show that the proposed solutions are efficient in reducing both the runtime and memory usage in mining periodic frequent patterns.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards fast and memory efficient discovery of periodic frequent patterns

Nofong

Wondoh

2019

Journal of Information and Telecommunication

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our productive pattern test in Definition 6 is the same as the productivity test proposed in [29,49] as follow: For any periodic itemset

X_{n}

\frac{| B S D | - a v g P e r (X_{n})}{a v g P e r (X_{n}) . | B S D |}

can be re-written as

\frac{| B S D | - a v g P e r (X_{n})}{a v g P e r (X_{n})} \times \frac{1}{| B S D |}

where

\frac{| B S D | - a v g P e r (X_{n})}{a v g P e r (X_{n})} = \frac{c o v_{B S D} (X_{n})}{| B S D |} = S u p_{S D B} (X_{n})

. Hence our productive-association test can be expressed as utilized in [49] as:

(\frac{| B S D | - a v g P e r (X)}{a v g P e r (X) . | B S D |}) > (\frac{| B S D | - a v g P e r (X_{1})}{a v g P e r (X_{1}) . | B S D |} \times | B S D | - a v g P e r (X_{2}) <...>

…”

Section: Proposed Modelmentioning

confidence: 99%

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care

Ismail

Hassan

2017

Sensors

View full text Add to dashboard Cite

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 for mining the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productive-associated 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 smart homes. 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.

show abstract