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

Yun, Unil; Nam, Hyoju; Kim, Jong‐Seong; Kim, Heonho; Baek, Yoonji; Lee, Judae; Yoon, Eunchul; Truong, Tin; Vo, Bay; Pedrycz, Witold

doi:10.1016/j.future.2019.09.024

Cited by 44 publications

(14 citation statements)

References 53 publications

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“…The PRE-HUI algorithm guarantees better performance than the FUP-HU, but it is not an accurate method because the mode loss may be due to the way the lower threshold is set. HUIPRED [7] effectively performs pattern extraction using a pre-large method. In other words, the proposed method reduces the rescan of the entire database by using two thresholds.…”

Section: A Incremental Methodsmentioning

confidence: 99%

A Survey of Key Technologies for High Utility Patterns Mining

et al. 2020

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Recently, high utility pattern mining (HUPM) is one of the most important research issues in data mining. Because it can consider the non-binary frequency values of items in a transaction and the different profit values of each item. It has been widely used. First of all, this paper briefly describes the related concepts, formulas and examples of application for HUPM. Secondly, the key technologies for HUMP are introduced in detail, and they are divided into main methods including Apriori-based, tree-based, projection-based, list-based, data format-based, and index-based and so on. The paper further compares data sets, uses, advantages and disadvantages of algorithms, laid the foundation for the next research direction. Then, this article outlines the high utility derivative patterns, including high average utility pattern, high utility sequential pattern, and high utility compact pattern and so on. Because static data is difficult to meet the actual needs, this paper summarizes the efficient use of HUPMs' methods over data streams, mainly based on incremental methods, based on the sliding window model methods, based on the time decay model methods and based on the landmark model methods and so on. INDEX TERMS Survey, pattern mining, high utility pattern, data streams, incremental databases.

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Section: A Incremental Methodsmentioning

confidence: 99%

A Survey of Key Technologies for High Utility Patterns Mining

et al. 2020

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“…DHUPL prunes patterns using a damped window model that considers newly arrived data as more important than previous data [22]. Also, to handle dynamic databases where transactions are frequently deleted, Yun et al adapted a pre-large method to mine HUIs efficiently while reducing the number of database scans to update results [23]. The SPHUI-Miner [24] algorithm, proposed by Bai et al introduced an efficient and compact data format named HUI-TRPL to reduce memory consumption.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Nguyen

et al. 2020

IEEE Access

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Analyzing customer transactions to discover high-utility itemsets is a popular task, which consists of finding the sets of items that are purchased together and yield a high profit. However, many studies assume that transactional data is static while in real-life, it changes over time. For example, the unit profits of items may vary from one week to another because sale prices and production costs may change. Many algorithms for mining high-utility itemsets (HUI) ignore this important property and thus are inapplicable or generate inaccurate results on real data. To address this issue, this paper proposes a novel algorithm named Multi-Core HUI Miner (MCH-Miner). It adapts techniques introduced in the iMEFIM algorithm to run on a parallel multi-core architecture to efficiently mine HUIs in dynamic transaction databases. An empirical evaluation shows that in most cases, MCH-Miner is significantly faster than iMEFIM, and that the cost of database scans is reduced. INDEX TERMS Data mining, high utility itemset, dynamic profit, parallel, multithread.

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“…Recently, two tree-based techniques have been proposed to find meaningful patterns using pre-large concept, which classifies patterns into three types, to efficiently process dynamic databases. PIHUP [29] extracts high utility patterns from an incremental database, and HUIPRED [30] finds high utility patterns from a dynamic database where transactions are deleted over time. The aforementioned techniques are only suitable for processing dynamic databases that do not consider the arrival time of data.…”

Section: B High Utility Pattern Mining From Dynamic Databasesmentioning

confidence: 99%

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Nam

Yun

et al. 2020

IEEE Access

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Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem, the high utility pattern mining approaches from non-binary database have been proposed and actively studied by many researchers. Lately, new data is progressively created with the passage of time in diverse area such as biometric data of a patient diagnosed in a medical device and log data of an internet user, and the volume of a database is gradually increasing. A database with these characteristics is called a dynamic database. Under these circumstances, high utility mining techniques suitable for analyzing dynamic databases have recently been extensively studied. In this paper, we propose a new list-based algorithm that mines high utility patterns considering the arrival time of each transaction in an incremental database environment. That is, our algorithm efficiently performs pattern pruning by using a damped window model that considers the importance of the previously inputted data lower than that of recently inserted data and identifies high utility patterns. Experimental results indicate that our proposed method has better performance than the state-of-the-art techniques in terms of runtime, memory, and scalability. INDEX TERMS Data mining, damped window model, pattern pruning, high utility patterns, stream data mining.

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

Cited by 44 publications

References 53 publications

A Survey of Key Technologies for High Utility Patterns Mining

A Survey of Key Technologies for High Utility Patterns Mining

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

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