Damped window based high average utility pattern mining over data streams

Yun, Unil; Kim, Dong-Gyu; Yoon, Eunchul; Fujita, Hamido

doi:10.1016/j.knosys.2017.12.029

Cited by 110 publications

(42 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HAUIM divides the utility of an itemset by its length (the number of items that the itemset contains). Up to now, some interesting works have been extensively studied, such as Apriori-based algorithms [70], projection-based PAI [94], utility-list based HAUI-Miner [95], [96], and other hybrid algorithms with different upper-bound models [96], [97].…”

Section: Mining High Average Utility Itemsetsmentioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

230

148

View full text Add to dashboard Cite

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

show abstract

Section: Mining High Average Utility Itemsetsmentioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

230

148

View full text Add to dashboard Cite

show abstract

“…Liu et al [8] designed an algorithm for mining HUIs, although this needs to scan the database multiple times and generates many candidates. Many approaches have thus been put forward to avoid large numbers of database scans and the generation of numerous candidates, such as those using the incremental high-utility pattern (IHUP) [17], fast high-utility miner (FHM) [18], efficient high-utility itemset mining (EFIM) [10], high-utility itemset miner (HMiner) [19], utility-list buffer for high-utility itemset miner (ULB-Miner) [20], and sliding window based high-utility pattern mining (SHUPM) [21].…”

Section: High-utility Itemset Miningmentioning

confidence: 99%

“…These authors proposed the ULB-Miner algorithm, which uses a novel structure called a buffered utility list to reuse the memory allocation for the utility list, so that the set of HUIs can be returned quickly without the need for a large amount of memory. Yun et al [21] investigated the HUIM problem and proposed a representative algorithm called SHUPM. This new solution does not generate candidate itemsets, and hence reduces the search space so that the HUIM process is more effective in terms of runtime and memory usage.…”

Section: High-utility Itemset Miningmentioning

confidence: 99%

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Mai

Nguyen

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.

show abstract

“…Moreover, the most common outlier detection methods [7,[13][14][15][16] are directed toward static precise data (the data's existence or nonexistence has been determined), which are not suitable for uncertain data stream (each data element has an existential probability), although the data are only added as a probability attribute. Although window-based technologies such as sliding windows [20], damped windows [19] and landmark windows [17] provide good solutions for processing data stream, the large volume of data stream causes the frequent itemset mining processes to require considerable time cost. The multiple scanning of the data stream in methods such as some Apriori-like methods [5] and FP-growth-like methods [12,18,20] is also unrealistic in the era of big data.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Outlier Detection Approach Over Uncertain Data Stream Based on Frequent Itemset Mining

Hao

Cai

Sun

et al. 2019

ITC

View full text Add to dashboard Cite

Outlier detection is essential in data-based science. It aims to detect those itemsets that have a significant difference from the other data. With the limitations of equipment precision and network transmission, uncertain data are becoming more common in daily life. However, the traditional outlier detection methods are not applicable for uncertain data stream, and the large volume of data makes outlier detection costly in terms of memory usage and time. Moreover, the multiple scanning of the data stream required for Apriori-like methods is unrealistic. In this paper, a matrix structure is constructed to store the information of an uncertain data stream, and the subsequent mining process is conducted on the matrix structure; therefore, the whole data stream needs to be scanned only once. Then, the "upper cap" concept is used in the FIM-UDS method to mine the frequent itemsets more effectively to support outlier detection. Moreover, two outlier factors and an outlier detection method called FIM-UDSOD are designed to detect potential outliers. Finally, two public datasets are used to verify the efficiency of the FIM-UDS method, and one synthetic dataset is used to evaluate the FIM-UDSOD method. The experimental results show that our proposed FIM-UDSOD method is more effective than other methods in detecting outliers.

show abstract

Damped window based high average utility pattern mining over data streams

Cited by 110 publications

References 44 publications

A Survey of Utility-Oriented Pattern Mining

A Survey of Utility-Oriented Pattern Mining

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

An Efficient Outlier Detection Approach Over Uncertain Data Stream Based on Frequent Itemset Mining

Contact Info

Product

Resources

About