Development of a new metric to identify rare patterns in association analysis: The case of analyzing diabetes complications

Piri, Saeed; Delen, Dursun; Liu, Tieming; Paiva, William

doi:10.1016/j.eswa.2017.09.061

Cited by 23 publications

(8 citation statements)

References 27 publications

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“…In addition to support and confidence, the significance of the generated association rules was then measured using lift (Ordonez et al, 2006). A rule with lift greater than 1 is regarded as a useful rule that supports decision making by providing extra information (Piri et al, 2018); thus, the lift threshold is set to be > 1.0 to identify meaningful rules. The association between LIS keywords and the citing disciplines/communities was analysed using two-dimensional crosstab analysis.…”

Section: Methodsmentioning

confidence: 99%

The impact of Chinese library and information science on outside disciplines: A citation analysis

Chen

Qiao

Chiu

et al. 2019

Journal of Librarianship and Information Science

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This paper uses citation analysis to measure the intellectual impact of Chinese library and information science on outside disciplines. It analyses 469 journals in disciplines outside the library and information science field containing citations to 20 Chinese library and information science journals from 1996 to 2015. It shows that Chinese library and information science mainly receives citations from related disciplines, such as business and management, communication, and computer science, and that the majority of library and information science articles are rarely cited. The library and information science subjects of bibliometrics, information technology and knowledge management are most likely to be cited.

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Section: Methodsmentioning

confidence: 99%

The impact of Chinese library and information science on outside disciplines: A citation analysis

Chen

Qiao

Chiu

et al. 2019

Journal of Librarianship and Information Science

View full text Add to dashboard Cite

show abstract

“…Meanwhile, some methods are derived from different tasks, such as negative sequential pattern mining [ 14 , 15 ], frequent pattern discovery with tri-partition alphabets [ 51 ], utility pattern mining [ 52 – 57 ], and contrast pattern mining [ 58 , 59 ]. Moreover, some methods are derived from different characteristics of patterns, such as frequent pattern mining [ 60 ], rare pattern mining [ 61 ], top-k pattern mining [ 16 ], closed pattern mining [ 62 ], and maximal pattern mining [ 63 , 64 ].…”

Section: Related Workmentioning

confidence: 99%

NetNMSP: Nonoverlapping maximal sequential pattern mining

Zhang

Guo

et al. 2022

Appl Intell

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Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

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“…Association analysis utilizes data mining methods to extract the relationships/affinity patterns/rules among various items objects or events.In recent years several implications for association analysis has been demonstrated, primarily focusing on support-confidence theory, in various domains including market analysis for extracting consumer purchase patterns with buying products [23], social media mining for identifying topics in tweets [24], recommendation systems [25], [26] and health-care [27]. [27] performed association analysis on electronic medical records of diabetes patients and proposed a new assessment metric to identify rare items/patterns without over-generating association rules. Researchers [28] have developed a hierarchical matrix-based visualization technique employing Apriori algorithm for mining association rules in categorical datasets.…”

Section: Associations Extraction For Crime Analysismentioning

confidence: 99%

An interactive human centered data science approach towards crime pattern analysis

Qazi

Wong

2019

Information Processing & Management

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Development of a new metric to identify rare patterns in association analysis: The case of analyzing diabetes complications

Cited by 23 publications

References 27 publications

The impact of Chinese library and information science on outside disciplines: A citation analysis

The impact of Chinese library and information science on outside disciplines: A citation analysis

NetNMSP: Nonoverlapping maximal sequential pattern mining

An interactive human centered data science approach towards crime pattern analysis

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