High Utility Patterns Research Articles

A vulnerability spatiotemporal distribution prognosis framework for integrated energy systems within intricate data scenes according to importance-fuzzy high-utility pattern identification

One key task for the planning and organizing of Inspection and Maintenance (I&M) works in an integrated energy system (IES) is to handle its weakest vulnerabilities, that is, when and where in this system the I&M actions or countermeasures ought to be performed. This requires a pinpoint spatiotemporal distribution prediction of these vulnerabilities, then more preparation times can be brought about for utilities to appropriately allocate the limited I&M manpower and equipment. With such a motivation, in light of both the internal and surrounding conditions, this paper proposes a prognosis framework, namely the Importance-Fuzzy High-utility Pattern Identification with lifetime-dependent factors (IFHPIlf). The IFHPIlf aims for the component-vulnerability patterns with higher profits rather than simply higher frequencies. In that case, when applied in imbalanced distributed databases, this framework can incorporate the High-Impact-Low-Probability (HILP) components as well, without the extra steps of separating and assessing rare and common components. For the purpose of assessing the utilities of each component and transaction, a parallel learning architecture is formulated to evaluate the corresponding two attributes, unit price and quantity, respectively. To estimate the unit price, the risk level of each component will be quantitatively rated via the built BP Time-dependent-lifetime Importance (BPTdlI) model, where the direct impacts of each component’s lifetime distribution on the reliability distribution of the whole system, as well as the entire underlying system failure-related hazard cut sets, are integrated. Ergo, the data dependence in temporal scales can be taken into account; To qualitatively differentiate the perilous components, the quantity will be calculated in the established Importance-Fuzzy High-utility Pattern Identification (IFHPI) model, wherein the time-dependent-lifetime important FIS are conjugated with the Fuzzy Interest-Utility Measures (FIUM). Hence, both the discrete and continuous features will be handled in the same entity, and the determinations can be straightforwardly based on their influences. At last, the flexibility and feasibility of this framework during applications are demonstrated in terms of an empirical case study.

Mining periodic trends via closed high utility patterns

High utility pattern mining (HUPM) plays a significant role in data mining technologies. Traditional HUPM algorithms may produce a large number of high utility patterns (HUPs) when the database is dense or the data is massive. To address this issue, closed high utility pattern (CHUP) mining was proposed, providing a high-level overview of the HUPs and helpful information for decision-makers. However, CHUPs do not consider the factors of period and recency. Therefore, this paper is the first to introduce period and recency into closed high utility pattern mining and proposes the CPR-Miner algorithm to mine closed periodic recent high utility patterns. These patterns have more practical value since they are closed sets of HUPs. Due to the increasing number of factors to be considered, new upper bounds and pruning strategies are also proposed, significantly improving the algorithm’s efficiency. To test the performance of our algorithm and our new pruning strategies, we improved the PHM algorithm to generate the PR-Miner algorithm. Experimental results show a significant efficiency of the new pruning strategies and demonstrate that CPR-Miner outperforms the PR-Miner algorithm in all aspects.

Extended High-Utility Pattern Mining: An Answer Set Programming-Based Framework and Applications

AbstractDetecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.

Mining periodic high-utility itemsets with both positive and negative utilities

Mining high-utility patterns in databases containing items with both positive and negative profits is useful in market basket databases, since negative profits are common in the real world. Obviously, in the market basket database, patterns with stable long-term profits have more meaning. The discovery of itemsets with a consistent high frequency is known as periodic frequent pattern mining. Therefore, mining periodic high-utility patterns in a database containing items with both positive and negative profits is an interesting and useful task. However, this task has two main challenges. First, the utility measure does not have the download closure property. Second, the huge search space needs to be pruned more effectively. In this paper, we propose a vertical data structure-based algorithm called PHMN to discover periodic high-utility patterns (PHUPs) or itemsets in a transaction database with both positive and negative utilities. To be more efficient, we propose a new upper bound to prune the search space and an improved algorithm to discover the PHUPs. Finally, experiments are conducted to verify the effectiveness and efficiency of algorithms.

Pre-large based high utility pattern mining for transaction insertions in incremental database

High utility pattern mining has been actively researched and applied to diverse applications because it can process the database by considering the quantity and importance of items. However, traditional high utility pattern mining methods aim to handle static databases, so they cannot meet the requirements of users who want to process the dynamic environments. Although methods to process incremental databases have been proposed, they have limitations that they perform the mining process on the entire database, including already processed data, whenever data are newly inserted. The pre-large concept is one of the techniques to process the dynamic database. Utilizing the pre-large technique, we can efficiently handle the transaction insertion using the extracted patterns of the previous mining process. In this paper, we propose a novel pre-large-based approach to discover high utility patterns from incremental databases. A list structure is proposed to store the utility information of patterns, so candidate patterns are not generated, and an additional database scan is not required. Performance evaluation performed on various real and synthetic datasets shows that the proposed algorithm is more efficient and effective than the latest approaches in a dynamic environment.

A Study of Various Research Opportunities for High Utility Pattern Mining Techniques

A Study of Various Research Opportunities for High Utility Pattern Mining Techniques

Mining Significant Utility Discriminative Patterns in Quantitative Databases

Drawing a discriminative pattern in quantitative datasets is often represented to return a high utility pattern (HUP). The traditional methods output patterns with a utility above a pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting the results in a timely manner to strengthen the interaction between the mining system and users. Pattern sampling can return results with a probability guarantee in a short time, and it could be a candidate technology to mine such discriminative patterns. In this paper, a novel approach named HUPSampler is proposed to sample one potential HUP, which is extracted with probability significance according to its utility in the database. HUPSampler introduces an interval constraint on the length of HUP and randomly extracts an integer k according to the utility proportion firstly; then, the HUPs could be obtained efficiently from a random tree by using a pattern growth way, and finally, it returns a HUP of length k randomly. The experimental study shows that HUPSampler is efficient in regard to memory usage, runtime, and utility distribution. In addition, case studies show that HUPSampler can be significantly used in analyzing the COVID-19 epidemic by identifying critical locations.

FCHM-stream: fast closed high utility itemsets mining over data streams

The high-speed, continuous and endless characteristics of data streams make it a challenging task to quickly mine high utility itemsets in limited memory space. The sliding window model, which focuses only on the most recent data, has received extensive research and attention as it can effectively adapt to the data stream environment. However, the presence of many communal batches in adjacent sliding windows causes the algorithm to repeatedly generate a large number of identical itemsets, which reduces the spatiotemporal performance of the algorithm. In order to solve these problems and provide users with a concise and lossless resultset, a new closed high utility pattern mining algorithm over data stream is proposed, named FCHM-Stream. A new utility list structure based on batch division and a resultset maintenance strategy based on skip-list structure are designed to effectively reduce identical itemsets repeatedly generated and thus reduce the running time of the algorithm. Extensive experimental results show that the proposed algorithm has a large improvement in runtime compared to the state-of-the-art algorithms.

High Utility Pattern Mining: A Survey on Current and Possible Areas of Applications

High Utility Pattern Mining (HUPM) has a wide range of applications, including making recommendations, detecting outliers, analyzing customer behaviors, and solving a wide range of other problems. In fact, unlike other important data mining tasks such as outlier analysis and classification, high utility pattern mining can be used as an intermediary tool for providing pattern-centered insights for other data mining tasks. In this paper, we look at a wide range of different applications of high utility pattern mining that are available in the literature. We gathered the literature review papers from different resources based on the keywords related to the applications of high utility pattern mining. Then we classified the existing research papers into different categories according to the domain application, then we summarize the proposed method of each paper and the datasets used for the evaluation process. Rather than providing a full discussion of every possible application, the objective of this paper is to give readers an overview of what is feasible when high utility pattern mining approaches are used. This paper can help researchers to apply the techniques of high utility pattern mining to solve real-time problems and it can help to explore other possible domains to apply high utility pattern mining techniques by proposing new methods and evaluating them accordingly.

Mining for Enthalpy-Based Average High-Utility Patterns with Tighter Upper Bounds

Mining for Enthalpy-Based Average High-Utility Patterns with Tighter Upper Bounds

Mining Statistically Significant Patterns with High Utility

Statistically significant pattern mining (SSPM) is to mine patterns with significance based on hypothesis test. Under the constraint of statistical significance, our study aims to introduce a new preference relation into high utility patterns and to discover high utility and significant patterns (HUSPs) from transaction datasets, which has never been considered in existing SSPM problems. Our approach can be divided into two parts, HUSP-Mining and HUSP-Test. HUSP-Mining looks for HUSP candidates and HUSP-Test tests their significance. HUSP-Mining is not outputting all high utility itemsets (HUIs) as HUSP candidates; it is established based on candidate length and testable support requirements which can remove many insignificant HUIs early in the mining process; compared with the traditional HUIs mining algorithm, it can get candidates in a short time without losing the real HUSPs. HUSP-Test is to draw significant patterns from the results of HUSP-Mining based on Fisher’s test. We propose an iterative multiple testing procedure, which can alternately and efficiently reject a hypothesis and safely ignore the hypotheses that have less utility than the rejected hypothesis. HUSP-Test controls Family-wise Error Rate (FWER) under a user-defined threshold by correcting the test level which can find more HUSPs than standard Bonferroni’s control. Substantial experiments on real datasets show that our algorithm can draw HUSPs efficiently from transaction datasets with strong mathematical guarantee.

Mining closed high utility patterns with negative utility in dynamic databases

Mining closed high utility patterns with negative utility in dynamic databases

Efficient approach of sliding window-based high average-utility pattern mining with list structures

Data mining has been actively studied, and it has become more important due to the development of information technology and the demands of diverse applications, such as the retail market, medical field, and manufacturing plants. High average-utility pattern mining is a type of data mining that finds valuable patterns by dividing the utility of a pattern by the length of the pattern. It considers the length of the pattern, so it can handle the demerits of utility pattern mining and extract more important patterns. Meanwhile, stream data is produced in real time, and the importance of the recent data gradually increases as opposed to the old data. However, most of the previous high average utility pattern mining approaches cannot find the latest valuable patterns over the data streams. Although research using a sliding window model was conducted to handle only the recent data, it has a problem due to the data structure. Since the previous work is a tree-based approach, numerous candidate patterns are generated, and an additional database scan is required to calculate the actual utilities of candidates. Creating the candidate patterns reduces the efficiency of runtime and memory and makes real-time data processing difficult. We propose an efficient algorithm that uses a list structure for sliding window-based high average-utility pattern mining in this paper. The proposed algorithm scans the stream data once, mines the complete set of the recent high average-utility patterns without generating candidates, and reduces the search space by using a novel pruning technique. The experiment results show that the proposed algorithm outperforms the state-of-the-art algorithms in terms of the runtime, memory, and scalability in real and synthetic datasets.

EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits

High-utility pattern mining is an important sub-literature in the data mining literature. This literature discusses the discovery of useful pattern information from large databases by considering not only supports of patterns but also profits and quantities of items. This literature has the potential to be applied to various problems in the real world, so many methods for the improvement of the algorithm performance have been studied. Moreover, there have also been attempts to extend the flexibility of this literature. The traditional approaches in this literature considered the positive unit profits of items in a given database only. However, this literature can take extended flexibility into account by considering negative as well as positive unit profits of the items. In this paper, we suggest an efficient approach for mining high-utility patterns with negative unit profits. Moreover, the experimental performance tests, which are performed on various real and synthetic datasets in this paper, show that the proposed algorithm has a better performance than the state-of-the-art methods in this literature in terms of the runtime, memory usage, and scalability.

An Enhanced Fast – High Utility Item set Mining Method for Large Datasets

High Utility Itemset mining is considered one of the critical and challenging problems in data mining. The existing mining framework is limited to analyzing occurrence counts of items in the Database. However, this framework applies a single minimum utility threshold value that fails to consider different item characteristics. Recent methods of association mining focused on finding the high utility itemsets instead of frequent itemsets generations. Some utility-based mining methods that is Faster High Utility Itemset Mining (FHM), High Utility Itemset Miner (HUI-Miner), Direct Discovery of High Utility Patterns (D2HUP), Utility Pattern Growth (UP Growth & UP Growth+) are studied for the generation of high utility itemsets generations. Existing HUI mining methods are effectively generating HUIs. However, developing a faster and memory-efficient HUI mining method is required. For this purpose, this work develops an Enhanced Fast - High Utility Itemset Mining (EF-HUIM) method for the faster generation of high utility itemsets and respective association rules.

Analysis of Special Children Education Using Data Mining Approach

Data mining is a method that gives valuable information where we can improve and achieve the goal. Existing data mining techniques are applied in education for analyzing the performance of students, classes, and institutions. This helps the teachers and management to identify where they lag and improvise. Some mining techniques are used to predict and identify special children’s categories. In this paper, data mining techniques are applied for the special children to predict the achievement in special school study for child categories like Mental Retardation (MR), autism, and cerebral palsy using the collected assessment detail. Vocational training is considered an essential aspect for special school children for their future survival. Data mining methods are applied to mine the most achievable and essential training factors as a pattern by apriori rule mining and high utility pattern mining algorithms, based on their assessment achieved from the age 10–14, where Madras developmental programming scale is followed in special schools. We can predict which category children are achieving necessary factors, which helps identify the alternate methods to train the particular activity. Essential factors that cannot achieve at the same level will be trained in the next prevocational level to reach their vocational training. The prediction that we have made will be helpful for the teachers to train the children concerning their achievements.

Healthcare utilization trajectory among survivors of colorectal cancer.

To examine healthcare utilization patterns among patients < 65 y with colorectal cancer (CRC) from pre-diagnosis to 3 y into survivorship. Truven Health Analytics MarketScan Commercial Claims and Encounters Database was used to identify patients diagnosed with non-metastatic CRC between 2014 and 2016, with follow-up until 12/31/2019. Total visits (inpatient and outpatient) were estimated for 6months intervals from 2 y to 1months prior to CRC diagnosis (pre-cancer phase) and from a 1-y post-cancer diagnosis to a 3-y post-cancer diagnosis (survivorship phase). Utilization patterns from pre- to post-diagnosis were defined using median and 75th percentile visit counts. Interrupted time series (ITS) analyses examined pre- and post-cancer diagnosis utilization trends. Multivariable regression models estimated pre-cancer factors associated with high and low utilization patterns. Median age at CRC was 54 y (21-62); 50.6% of the patients were male, 30.9% were diagnosed with rectal cancer. ITS analyses demonstrated four utilization patterns with distinct pre- and post-cancer diagnosis utilization trends. Rectal cancer (RR = 1.13, p < 0.001) and high pre-cancer utilization (RR = 2.05, p < 0.001) were associated with a greater risk of high survivor phase utilization. Gastrointestinal conditions accounted for the greatest proportion of visits in pre-cancer phase (18%) and survivorship (17%), followed by cardiovascular disease (10% and 8%). Distinct patterns of healthcare utilization are observed both in the pre-cancer phase and survivorship phase of colorectal cancer and are influenced by cancer location, age, therapeutic exposures, and prior healthcare needs. Not all patients will require the same level or type of long-term follow-up. Identifying indication-specific healthcare utilization patterns that provide evidence for risk stratification may facilitate a more patient-centric and economically sustainable way to deliver care.

Frequent high-utility sequential pattern mining algorithm for integrated architecture design of multi-platform mission system

Frequent high-utility sequential pattern mining algorithm for integrated architecture design of multi-platform mission system

An Efficient Approach for Mining Reliable High Utility Patterns

Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold. However, a significant number of patterns will be generated if this threshold is set too low, which is an inherent limitation of these algorithms. This may cause the mining process to be inefficient as it would be difficult to analyze the patterns found. Furthermore, most of these patterns are unreliable and hard to be employed in making decisions. This paper proposed a novel problem of mining reliable high utility patterns by adapting the concept of reliability to mine a significant type of pattern called reliable high utility patterns. To address this issue, an efficient approach named RUPM (Reliable Utility-based Pattern Mining) is presented. RUPM introduces three novel measurements for estimating the reliability of utility-based patterns and proposes several strategies to efficiently handle reliable patterns with high utility values. Experimental results suggest that up to 99% of the patterns discovered by existing traditional high utility pattern mining algorithms were, in fact, unreliable. In contrast, the average reliability proportion in the resultant patterns obtained from the RUPM approach is at least 47.6% higher. Moreover, the proposed pruning strategies provide a reduction in both the runtime and memory usage.

Discovering Approximate and Significant High‐Utility Patterns from Transactional Datasets

Mining high‐utility pattern (HUP) on transactional datasets has been widely discussed, and various algorithms have been introduced to settle this problem. However, the time‐space efficiency of the algorithms is still limited, and the mining system cannot provide timely feedback on relevant information. In addition, when mining HUP from taxonomy transactional datasets, a large portion of the quantitative results are just accidental responses to the user‐defined utility constraints, and they may have no statistical significance. To address these two problems, we propose two corresponding approaches named Sampling HUP‐Miner and Significant HUP‐Miner. Sampling HUP‐Miner pursues a sample size of a transitional dataset based on a theoretical guarantee; the mining results based on such a sample size can be an effective approximation to the results on the whole datasets. Significant HUP‐Miner proposes the concept of testable support, and significant HUPs could be drawn timely based on the constraint of testable support. Experiments show that the designed two algorithms can discover approximate and significant HUPs smoothly and perform well according to the runtime, pattern numbers, memory usage, and average utility.