Frequent itemset mining: A 25 years review

Luna, José María; Fournier-Viger, Philippe; Ventura, Sebastián

doi:10.1002/widm.1329

Cited by 170 publications

(71 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…threshold of support and confidence) for rule development. Although we have used the Apriori algorithm for our association rules implemented in implemented in arules package, other researchers with large datasets may consider using other, potentially more efficient association rule mining algorithms for rule development [29,30]. We have implemented several methods to filter rules to ensure the uniqueness and robustness of rules to minimize the number of rules for panel review based on domain knowledge and statistical assessment, and may therefore have excluded useful rules during the rule cleaning process.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of data quality rules in administrative health data using association rule mining

Peng

Lee

D’Souza

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background: Data quality assessment presents a challenge for research using coded administrative health data. The objective of this study is to develop and validate a set of coding association rules for coded diagnostic data. Methods: We used the Canadian re-abstracted hospital discharge abstract data coded in International Classification of Disease, 10th revision (ICD-10) codes. Association rule mining was conducted on the re-abstracted data in four age groups (0-4, 20-44, 45-64; ≥ 65) to extract ICD-10 coding association rules at the three-digit (category of diagnosis) and four-digit levels (category of diagnosis with etiology, anatomy, or severity). The rules were reviewed by a panel of 5 physicians and 2 classification specialists using a modified Delphi rating process. We proposed and defined the variance and bias to assess data quality using the rules. Results: After the rule mining process and the panel review, 388 rules at the three-digit level and 275 rules at the four-digit level were developed. Half of the rules were from the age group of ≥65. Rules captured meaningful agespecific clinical associations, with rules at the age group of ≥65 being more complex and comprehensive than other age groups. The variance and bias can identify rules with high bias and variance in Alberta data and provides directions for quality improvement. Conclusions: A set of ICD-10 data quality rules were developed and validated by a clinical and classification expert panel. The rules can be used as a tool to assess ICD-coded data, enabling the monitoring and comparison of data quality across institutions, provinces, and countries.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and validation of data quality rules in administrative health data using association rule mining

Peng

Lee

D’Souza

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Several other algorithms were also developed in the literature [9][10][11]18] to find frequent patterns. Luna et al [12] conducted a detailed survey on frequent pattern mining and presented the improvements that happened in the past 25 years. However, frequent pattern mining is inappropriate for identifying patterns that are regularly appearing in a temporal database.…”

Section: Frequent Pattern Miningmentioning

confidence: 99%

“…Several competent techniques were discussed in the literature [9][10][11] to enumerate all frequent patterns from a transactional database. Luna et al [12] recently presented a survey on the advances that happened in the past 25 years of frequent pattern mining. The popular adoption and the successful adoption of this technique has been hindered by its limitation to discover regularities that may exist in a temporal database.…”

Section: Introductionmentioning

confidence: 99%

Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

et al. 2021

View full text Add to dashboard Cite

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

show abstract

“…Zaki and Gouda [21] propose an algorithm based on diffset data structure, dECLAT, which tracks only the difference of candidate patterns in producing frequent patterns, which greatly reduces the memory needed to store intermediate results. Uno et al [22] propose an algorithm LCM based on enumeration of reserved extensions, which is efficient and one of the most important ones so far [23].…”

Section: Related Workmentioning

confidence: 99%

Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment

et al. 2020

View full text Add to dashboard Cite

Association rules can detect the association pattern between POIs (point of interest) and serve the application of indoor location. In this paper, a new index, tuple-relation, is defined, which reflects the association strength between POI sets in indoor environment. This index considers the potential association information such as spatial and semantic information between indoor POI sets. On this basis, a new R-FP-growth (tuple-relation frequent pattern growth) algorithm for mining association rules in indoor environment is proposed, which makes comprehensive use of the co-occurrence probability, conditional probability, and multiple potential association information among POI sets, to form a new support-confidence-relation constraint framework and to improve the quality and application value of mining results. Experiments are performed, using real Wi-Fi positioning trajectory data from a shopping mall. Experimental results show that the tuple-relation calculation method based on cosine similarity has the best effect, with an accuracy of 87%, and 19% higher than that of the traditional FP-growth algorithm. INDEX TERMS Association rule, data mining, indoor trajectory, network embedding.

show abstract

Frequent itemset mining: A 25 years review

Cited by 170 publications

References 76 publications

Development and validation of data quality rules in administrative health data using association rule mining

Development and validation of data quality rules in administrative health data using association rule mining

Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment

Contact Info

Product

Resources

About