Mining high utility pattern with negative items in dynamic databases

2021 ◽  
Author(s):  
Meng Han ◽  
Ni Zhang ◽  
Le Wang ◽  
Xiaojuan Li ◽  
Haodong Cheng
Keyword(s):  
Dynamic Databases ◽  
High Utility

scholarly journals Dynamic maintenance model for high average-utility pattern mining with deletion operation

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Qian Teng ◽  
Shahab Tayeb ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Pattern Mining ◽  
Computational Cost ◽  
Practical Applications ◽  
Itemset Mining ◽  
Dynamic Databases ◽  
Speed Up ◽  
Dynamic Maintenance ◽  
Average Utility ◽  
High Utility ◽  
Maintenance Model

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

scholarly journals Incrementally updating the high average-utility patterns with pre-large concept

2020 ◽  
Vol 50 (11) ◽  
pp. 3788-3807
Author(s):  
Jerry Chun-Wei Lin ◽  
Matin Pirouz ◽  
Youcef Djenouri ◽  
Chien-Fu Cheng ◽  
Usman Ahmed
Keyword(s):  
State Of The Art ◽  
The State ◽  
Batch Mode ◽  
Itemset Mining ◽  
The Past ◽  
Dynamic Databases ◽  
Speed Up ◽  
Average Utility ◽  
High Utility ◽  
High Utility Patterns

Abstract High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

Incrementally Mining High Utility Itemsets in Dynamic Databases

2010 ◽  
Author(s):  
Chun-Wei Lin ◽  
Tzung-Pei Hong ◽  
Guo-Cheng Lan ◽  
Hsin-Yi Chen ◽  
Hung-Yu Kao
Keyword(s):  
Dynamic Databases ◽  
High Utility ◽  
High Utility Itemsets

Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases

2015 ◽  
Vol 29 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Chun-Wei Lin ◽  
Tzung-Pei Hong ◽  
Guo-Cheng Lan ◽  
Jia-Wei Wong ◽  
Wen-Yang Lin
Keyword(s):  
Dynamic Databases ◽  
High Utility ◽  
High Utility Itemsets

Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases

2020 ◽  
Vol 103 ◽  
pp. 58-78 ◽  
Author(s):  
Unil Yun ◽  
Hyoju Nam ◽  
Jongseong Kim ◽  
Heonho Kim ◽  
Yoonji Baek ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Dynamic Databases ◽  
High Utility

Impact of long-term trials on crop production research and education

2010 ◽  
Vol 58 (Supplement 1) ◽  
pp. 1-5 ◽  
Author(s):  
M. Jolánkai ◽  
F. Nyárai ◽  
K. Kassai
Keyword(s):  
Crop Production ◽  
Life Sciences ◽  
Ecological Models ◽  
Dynamic Databases ◽  
Production Research ◽  
Research And Education

Long-term trials have a twofold role in life sciences, acting as both live laboratories and public collections. Long-term trials are not simply scientific curios or the honoured relics of a museum, but highly valuable live ecological models that can never be replaced or restarted if once terminated or suspended. These trials provide valuable and dynamic databases for solving scientific problems. The present paper is intended to give a brief summary of the crop production aspects of long-term trials.

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