scholarly journals Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets

2021 ◽  
Author(s):  
Roy Setiawan ◽  
Dac-Nhuong Le ◽  
Regin Rajan ◽  
Thirukumaran Subramani ◽  
Dilip Kumar Sharma ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
Utility Mining ◽  
High Utility

scholarly journals On-Shelf Utility Mining of Sequence Data

2021 ◽  
Vol 16 (2) ◽  
pp. 1-31
Author(s):  
Chunkai Zhang ◽  
Zilin Du ◽  
Yuting Yang ◽  
Wensheng Gan ◽  
Philip S. Yu
Keyword(s):  
High Efficiency ◽  
Sequence Data ◽  
Real Life ◽  
Search Space ◽  
Upper Bounds ◽  
Utility Mining ◽  
Limited Memory ◽  
Time Periods ◽  
High Utility ◽  
Synthetic Datasets

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this article, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS + , to extract on-shelf high-utility sequential patterns. For further efficiency, we also design several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility ( TPEU ) and time reduced sequence utility ( TRSU ). In addition, two novel data structures are developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS + has wider real-life applications owing to its high efficiency.

Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility

2019 ◽  
pp. 191-203 ◽  
Author(s):  
R. Uday Kiran ◽  
T. Yashwanth Reddy ◽  
Philippe Fournier-Viger ◽  
Masashi Toyoda ◽  
P. Krishna Reddy ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
High Utility

scholarly journals An Incremental High-Utility Mining Algorithm with Transaction Insertion

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Wensheng Gan ◽  
Tzung-Pei Hong ◽  
Binbin Zhang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Incremental Algorithm ◽  
Rule Mining ◽  
Utility Mining ◽  
Real World Applications ◽  
Enumeration Tree ◽  
Speed Up ◽  
High Utility ◽  
Very Large Database

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.

Ordering policy estimation for high utility item-sets considering negative item values in large databases

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Real World ◽  
Defective Items ◽  
Utility Mining ◽  
Mining Algorithm ◽  
Negative Item ◽  
Ordering Policy ◽  
Large Databases ◽  
Real World Applications ◽  
High Utility

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results

An Efficient Method for Protecting High Utility Itemsets in Utility Mining

2016 ◽  
Vol 9 (7) ◽  
pp. 147-156
Author(s):  
Anshu Chaturvedi ◽  
D. N. Goswami ◽  
Rishi Soni
Keyword(s):  
Efficient Method ◽  
Utility Mining ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals High utility-itemset mining and privacy-preserving utility mining

2016 ◽  
Vol 7 ◽  
pp. 74-80 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Wensheng Gan ◽  
Philippe Fournier-Viger ◽  
Lu Yang ◽  
Qiankun Liu ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Utility Mining ◽  
Itemset Mining ◽  
High Utility

scholarly journals Towards Efficient Mining of Periodic High-Utility Itemsets in Large Databases

2019 ◽  
Vol 8 (4) ◽  
pp. 8083-8091
Keyword(s):  
Computational Cost ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Large Databases ◽  
Very Large Databases ◽  
Frequent Itemsets Mining ◽  
Mining Methods ◽  
High Utility ◽  
High Utility Itemsets ◽  
Temporal Periodicity

High Utility Item sets mining has attracted many researchers in recent years. But HUI mining methods involves a exponential mining space and returns a very large number of high-utility itemsets. . Temporal periodicity of itemset is considered recently as an important interesting criteria for mining high-utility itemsets in many applications. Periodic High Utility item sets mining methods has a limitation that it does not consider frequency and not suitable for large databases. To address this problem, we have proposed two efficient algorithms named FPHUI( mining periodic frequent HUIs), MFPHM(efficient mining periodic frequent HUIs) for mining periodic frequent high-utility itemsets. The first algorithm FPHUI miner generates all periodic frequent itemsets. Mining periodic frequent high-utility itemsets leads to more computational cost in very large databases. We further developed another algorithm called MFPHM to overcome this limitation. The performance of the frequent FPHUI miner is evaluated by conducting experiments on various real datasets. Experimental results show that proposed algorithms is efficient and effective.

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