Efficient Chain Structure for High-Utility Sequential Pattern Mining

Lin, Jerry Chun‐Wei; Li, Yuanfa; Fournier-Viger, Philippe; Djenouri, Youcef; Ji, Zhang

doi:10.1109/access.2020.2976662

Cited by 29 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As examples of combining with other fields, utility pattern mining approaches 27–29 in a profit database where the weight of the items fluctuates with transaction are proposed. PUSP 30 and SU‐Chain 31 extract the utility patterns by considering the passage of time and the order that the items appeared.…”

Section: Related Workmentioning

confidence: 99%

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. In addition, utility occupancy pattern mining, which considers the quantity, importance, and proportion of the pattern in the transaction, has been proposed. Despite this advantage, there is no utility seizing approach to handle the dynamically generated data flows. As electronics are interconnected and intelligent systems are constructed, data is generated in real‐time and accumulated rapidly. Therefore, a method to read data immediately in a dynamic environment and efficiently analyze massive data is required. To overcome the limitations of the existing utility occupancy methods, we propose a novel mining approach, HUOMI, which performs quickly on an increasing database. The suggested algorithm has an optimized data structure and an improved pruning technique, which can respond to the dynamic environment promptly. To indicate the effectiveness of the proposed method, performance evaluations were conducted on real and synthetic data sets. In the experimental results, the suggested algorithm showed a better performance than the other state‐of‐the‐art algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…This approach is used in this study. There are several other algorithms to get high-utility sequential pattern including [39]- [41].…”

Section: Related Workmentioning

confidence: 99%

Online Retail Pattern Quality Improvement: From Frequent Sequential Pattern to High-Utility Sequential Pattern

Gunawan¹

2021

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

View full text Add to dashboard Cite

There has been a change in people's shopping behavior, especially during the Covid-19 pandemic, from what traditionally requires direct face-to-face meetings between sellers and buyers, to virtual face-to-face through various shopping media. All activities carried out by customers, click streams performed, items purchased, the number of items including the price will be recorded in a log. Activity records in the log are very useful to be able to find out the pattern of activity sequences from customers, especially the order of items purchased by customers. However, the management certainly needs more knowledge, not just the order of goods that are often purchased by customers. Does the order of items purchased also provide maximum profit? There have been many methods to get frequent sequential patterns from customer activities, but getting a pattern that chooses more quality by adding utility value needs to be considered. In this research, the method used to obtain frequent sequential patterns is using PrefixSpan (Prefixprojected Sequential PAtterN) and the method used to obtain a high-utility sequential pattern is the USpan (Utility Sequential PAtterN) method. USpan is applied to the BMS (Blue Martini DataSet) dataset, which is the dataset used in KDD (Knowledge Discovery in Databases) CUP 2000 which consists of clickstream data from an e-commerce. The experimental results show that the frequent sequential pattern will always appear in the highutility sequential pattern but not vice versa. It is certain that a high-utility pattern must be sequential, but a sequential pattern is not necessarily a high-utility sequential. From the results of the high-utility sequential pattern, it can be used as input to provide recommendations to customers to carry out the shopping process on items that can provide greater profits. The conclusion of the research conducted is that the high-utility sequential pattern mining can produce a higher quality pattern than just getting a frequent sequential pattern.

show abstract

“…The work in [8] considers the negative item value in HUSP mining and an algorithm named HUSPNIV is proposed to mine HUSP with a negative item value. The work in [9] proposes an efficient sequence-utility (SU)-chain structure to improve mining performance. The work in [10] extends the occupancy measure to assess the utility of patterns in transaction databases and proposes an efficient algorithm named high-utility occupancy pattern mining (HUOPM).…”

Section: High Utility Sequential Pattern Mining (Husp Mining)mentioning

confidence: 99%

“…It focuses on extracting subsequences with a high utility (importance) from quantitative sequential databases. Current HUSP mining algorithms, however, only consider occurring events and do not take non-occurring events into account, which results in the loss of a lot of useful information [9][10][11]. Thus, high utility negative sequential pattern (HUNSP) mining is proposed to address this issue by considering both occurring events and non-occurring events.…”

Section: Introductionmentioning

confidence: 99%

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Zhang

et al. 2020

Symmetry

View full text Add to dashboard Cite

As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.

show abstract

Efficient Chain Structure for High-Utility Sequential Pattern Mining

Cited by 29 publications

References 33 publications

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Online Retail Pattern Quality Improvement: From Frequent Sequential Pattern to High-Utility Sequential Pattern

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Contact Info

Product

Resources

About