Candidate Itemsets Research Articles

Development of a novel parent-report measure of food allergy impact.

The aim of this study was to develop an outcome measure for caregivers of children with food allergy. A community-informed measure development process was utilised to generate a novel parent-reported outcome tool for caregivers of children with food allergy. We conducted focus groups with parents and caregivers of food-allergic (n = 18) children, following which a team of coders identified themes and generated an initial set of candidate items. These items were reviewed by content-area experts (n = 10) using content validity indices. We then conducted a series of cognitive interviews with parents of children with food allergy (n = 12) validating the instrument draft. We describe the results of these development steps as well as future steps to produce the final, validated form of the measure. The resultant 91-item measure is intended for parents of children ages two to 12 years. This measure fills a unique need in the field by leveraging key stakeholders' perspectives to develop and subsequently validate a novel parent-reported measure of parent and family food allergy impacts of children ages two to 12 years old.

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

Improving Clinical Decision Making With a Two-Stage Recommender System.

Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses challenges due to the multifaceted nature of clinical data and the demand for tailored recommendations. In this article, we introduce a 2-Stage Recommendation framework for clinical decision-making, which leverages a publicly accessible dataset of electronic health records. In the first stage, a deep neural network-based model is employed to extract a set of candidate items, such as diagnoses, medications, and prescriptions, from a patient's electronic health records. Subsequently, the second stage utilizes a deep learning model to rank and pinpoint the most relevant items for healthcare providers. Both retriever and ranker are based on pre-trained transformer models that are stacked together as a pipeline. To validate our model, we compared its performance against several baseline models using different evaluation metrics. The results reveal that our proposed model attains a performance gain of approximately 12.3% macro-average F1 compared to the second best performing baseline. Qualitative analysis across various dimensions also confirms the model's high performance. Furthermore, we discuss challenges like data availability, privacy concerns, and shed light on future exploration in this domain.

The Application of Big Data in the Management of Ideological and Political Education in the Development of Education Network

Based on a brief analysis of the current situation of university education management and research on intelligent algorithms, this article constructs a university education management system based on big data. For the clustering and prediction modules in higher education management, corresponding algorithms are used for optimization design. A fuzzy clustering algorithm based on entropy weight is proposed to address the shortcomings of the C-means clustering algorithm. This algorithm adds weighting coefficients on the basis of improvement and continuously updates the clustering centers. The prediction module uses Apriori algorithm to map and compress the target transaction database, reduces the number of candidate item sets through pruning process, and designs simulation experiments to measure the performance of the algorithm. The simulation results show that the clustering results of this algorithm are closer to the actual clustering situation, with shorter running time and better algorithm performance.

Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization

SummaryMatrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.

Association rule mining of aircraft event causes based on the Apriori algorithm

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events.

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

Data mining is the process of obtaining valuable or significant information from a large-scale database. One significantarea of research in the field of data mining is association rules mining. Apriori algorithm is one of the classical algorithmsin the association rule mining field. This research analyses the basic ideas and shortcomings of the Apriori algorithm andcompares several different styles of its major improvement strategies. Then it suggests an improved version of the Apriorialgorithm that utilizes a dataset summarization method, an optimized database mapping technique, an intersection operation,and a joined optimization strategy. These enhancements aim to address the low performance and efficiency by reducing thegeneration of candidate itemsets and minimizing the execution time. This addresses the issues of generating numerous candidate itemsets and repeatedly scanning the transaction database. After implementing the optimized algorithm, to verify its effectiveness, it has been applied to a groceries dataset, which is for market basket analysis. The improved Apriori algorithm demonstrated significant enhancements over the original algorithm in terms of reduced candidate itemsets and running time, leading to improved algorithm efficiency.

The Research on the Improvement of FP-growth Algorithm

This paper focuses on the issue of low efficiency in the FP-growth algorithm for frequent pattern mining and proposes an improved algorithm, ICFM-growth. Experimental results demonstrate that the improved algorithm outperforms the FP-growth algorithm in terms of both runtime and space utilization. By studying the classical frequent pattern algorithms Apriori and FP-growth, the latter is an improved algo- rithm based on Apriori to address the problems of generating a large number of candidate itemsets and consuming significant memory space. While FP-growth exhibits superior mining efficiency compared to Apriori, it faces challenges when dealing with large and long transactional databases due to the construction of numerous FP-trees, which increases computational tasks and prolongs runtime, leading to lagging mining efficiency. To address this issue, the improved algorithm ICFM-growth is proposed. It constructs a co-occurrence frequency matrix to perform preliminary screening on the transaction set, focusing on high-frequency and co-occurring item pairs, thereby reducing unnecessary computa- tions. In the initial stage, as important item pairs have already been filtered, the algorithm can directly operate on these item pairs and items, rather than the entire dataset, thereby reducing the search space and computational complexity. Additionally, the structure of the FP-tree ena- blesthe algorithm to store and process data more efficiently, avoiding frequent scanning of the entire database, as seen in traditional Apriori algorithms. Finally, through simulation experiments on publicly available datasets such as Movie Data and House Data, validated using cross- validation, the ICFM-growth algorithm proves to be significantly superior to FP-growth in terms of time and space efficiency. It demonstrates faster runtime, lower memory consumption, and superior mining efficiency compared to FP-growth.

Development of an ultra-short measure of eight domains of health-related quality of life for research and clinical care: the patient-reported outcomes measurement information system® PROMIS®-16 profile.

We describe development of a short health-related quality of life measure, the patient-reported outcomes measurement information system® (PROMIS®)-16 Profile, which generates domain-specific scores for physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function, and fatigue. An empirical evaluation of 50 candidate PROMIS items and item pairs was conducted using data from a sample of 5775 respondents from Amazon's Mechanical Turk (MTurk). Results and item response theory information curves for a subset of item pairs were presented and discussed in a stakeholder meeting to narrow the candidate item sets. A survey of the stakeholders and 124 MTurk adults was conducted to solicit preferences among remaining candidate items and finalize the measure. Empirical evaluation showed minimal differences in basic descriptive statistics (e.g., means, correlations) and associations with the PROMIS-29 + 2 Profile, thus item pairs were further considered primarily based on item properties and content. Stakeholders discussed and identified subsets of candidate item pairs for six domains, and final item pairs were agreed upon for two domains. Final items were selected based on stakeholder and MTurk-respondent preferences. The PROMIS-16 profile generates eight domain scores with strong psychometric properties. The PROMIS-16 Profile provides an attractive brief measure of eight distinct domains of health-related quality of life, representing an ideal screening tool for clinical care, which can help clinicians quickly identify distinct areas of concern that may require further assessment and follow-up. Further research is needed to confirm and extend these findings.

Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm

This paper addresses several problems in the power system. Key challenges include low-power information integration, inappropriate system data management, inaccurate system data updating, and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally, we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore, we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time, and realize automatic diagnosis and analysis of faults through operation data.

Research on the Path Construction and Practice of Bidirectional Cultural Mutual Guidance in Higher Vocational English Course Civics Teaching under the Background of Big Data

Abstract This study delves into the effective integration of bi-directional cultural intercultural instruction with Civic and Political teaching in a higher vocational English course and analyzes it with an association rule mining algorithm. By screening the weighted candidate itemsets through the weighted support degree model, the study identifies the itemsets’ positive and negative correlation attributes and mines the association rules between English teaching and students’ bidirectional cultural interduction ability by combining with the decision support model. The article also analyzes the content of Civics and Chinese and foreign cultural elements in the English curriculum. It compares and examines the changes in students’ two-way cultural core literacy and its introduction effect. The results of the study show that after the bi-directional cultural Civics teaching in the English curriculum, students experienced significant changes in worldview, values and Chinese and foreign cultural cognition, with t-values of −4.312, −5.034, and −4.275, respectively, and p-values of less than 0.001. In addition, the students’ bi-directional cultural communication competence improved significantly, with scores ranging from 3.8 to 4.3. The study provides valuable insights into the promotion of Chinese-foreign cultural communication and the reform of higher vocational English teaching.

Mining High Utility Itemsets Using Prefix Trees and Utility Vectors

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> items generally results in a huge search space, composed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n}$</tex-math></inline-formula> itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

Item Selection Algorithm Based on Collaborative Filtering for Item Exposure Control

AbstractIn computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set of candidate items whose expected performance matches the examinee's current performance is selected. In the second stage, an item that is approximately matched to the examinee's observed performance is selected from the candidate set. The expected performance of an examinee on an item is predicted by autoencoders. Experiment results show that the proposed algorithm outperforms existing item selection algorithms in terms of item exposure while incurring only a small loss in measurement precision.

Amyotrophic Lateral Sclerosis-Bulbar Dysfunction Index-Remote: Test-Retest and Interrater Reliability of Candidate Items.

The primary aim of this study was to establish the reliability of candidate items as a step in the development of the Amyotrophic Lateral Sclerosis-Bulbar Dysfunction Index-Remote (ALS-BDI-Remote), a novel tool being developed for the detection and monitoring of bulbar signs and symptoms in remote settings. The set of candidate items included 40 items covering three domains: cranial nerve examination, auditory-perceptual evaluation, and functional assessment. Forty-eight participants diagnosed with ALS and exhibiting a range of bulbar disease severity were included. Data collection for each participant took place on Zoom over three sessions. During Session 1, the participants were instructed to adjust their Zoom settings and to optimize their recording environment (e.g., lighting, background noise). Their cognition and eating were screened to determine their ability to follow instructions and their eligibility to perform the swallowing and chewing tasks. During Session 2, two speech-language pathologists (SLPs) administered the tool consecutively to determine the items' interrater reliability. During Session 3, one of the SLPs readministered the tool within 2 weeks of Session 1 to assess test-retest reliability. The reliability of each item was estimated using weighted kappa and the percentage of agreement. To be considered reliable, the items had to reach a threshold of 0.5 weighted kappa or 80% percentage agreement (if skewed distribution of the scores) for both interrater and test-retest reliability. In total, 33 of the 40 candidate items reached the reliability cutoff for both reliability analyses. All assessment domains included reliable items. Items requiring very good visualization of structures or movements were generally less reliable. This study resulted in the selection of reliable items to be included in the next version of the ALS-BDI-Remote, which will undergo psychometric evaluation (reliability, validity, and responsiveness analyses). Additionally, the results contributed to our understanding of the remote administration of SLP assessments for telehealth applications.

An Efficient Task Synthesis Method Based on Subspace Differential Patterns for Arrangements of Event Intervals Mining in the Avionics Cloud System Architecture

Avionics Cloud is a new multi-platform avionics system architecture that provides dynamic access, resource pooling, intelligent scheduling, on-demand service and other cloud computing features. Using Avionics Cloud to rationalize the order of multi-flight platform task execution and realize multitask synthesis is a challenging problem. In this paper, we propose an Efficient Task Synthesis Method based on Subspace Differential Patterns for Arrangements of Event Intervals Mining-DiMining. For tasks executed in a multi-platform Avionics Cloud system with dynamic characteristics of time intervals, DiMining is proposed. The algorithm mines the differential frequent task execution event interval patterns related to execution efficiency from the scenario dataset with high execution efficiency and the scenario dataset with low execution efficiency in order to identify key task patterns related to execution efficiency and improve the task synthesis design efficiency of the multi-platform Avionics Cloud system. Furthermore, in order to improve the mining efficiency of the algorithm, this algorithm designs a variety of pruning strategies to ensure that two differential time interval patterns with high and low functional execution efficiency are mined at one time without preserving the set of candidate items. The experimental results show that the DiMining algorithm is more efficient than the traditional algorithm on the open dataset. The DiMining algorithm is used to mine the 350-field high-efficiency operation scenario dataset and the 350-field efficiency operation scenario dataset under the constructed typical UAV cluster co-detection task scenarios. Based on the simulation results, the DiMining algorithm is able to effectively support the design of multi-platform Avionics Cloud system task synthesis architecture and improve the efficiency of UAV cluster collaborative detection.

Penerapan Algoritma Hash Based dalam Penemuan Aturan Asosiasi Penjualan Tanaman Hias

Technology is very influential in the world of increasingly fierce business competition so that business people must find strategies to increase sales results in the midst of business competition. Ornamental plant sellers must be smart in managing stock and making strategies in selling ornamental plants. Transaction data can be processed into information needed to increase sales results, one of which can be used as an analysis of the rules of the buyer transaction association in purchasing ornamental plants so that it can be processed and can support decision making on ornamental plant supplies and can assist officers in recommending other ornamental plants to buyers in a cross selling strategy. Knowing the ornamental plants that are often purchased will be a top priority that must be provided so that there is no stock shortage. In this case, data mining is needed to manage sales transaction data for ornamental plants at the Sindy Flower Shop using a Hash Based algorithm. Hash Based Algorithm that can optimally determine the frequent itemset of candidate itemset. In its application in determining the rules for selling associations of ornamental plants by applying a Hash Based algorithm to obtain frequent itemsets for the 3-itemset Dahlia, Empasen and Melati which are a combination of 3-itemset ornamental plants which are prioritized in sales with a support value of 25% and confidence of 60%

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Performing in-situ analytics: Mining frequent patterns from big IoT data at network edge with D-HARPP

Big IoT data is inherently distributed, high-dimensional, irregular, and sparse in nature. Fog computing model in its original form is by no means the optimal solution for mining big IoT data. However, utilizing the network edge for mining tasks, such as enabling edge and IoT devices to mine locally frequent patterns can significantly improve the mining performance. Additionally, edge devices capable of performing distributed job processing could utilize the model to the fullest. But resource poorness of edge and IoT devices needs lightweight pattern mining algorithms. This paper presents Distributed HARnessing the Power of Powersets for Mining Frequent Itemsets (D-HARPP), a spark-based distributed algorithm to mine frequent co-occurring itemsets in big IoT data. Unlike state-of-the-art distributed algorithms, D-HARPP makes a single pass over the data and does not create candidate itemsets; thus, achieves significantly better runtime and consumes the least memory. Moreover, performance of D-HARPP is not deteriorated at lower minimum support thresholds. These distinguishing characteristics make D-HARPP an optimal choice for Spark-enabled edge and IoT devices. D-HARPP has outperformed Spark-Apriori, another distributed algorithm by significant margins, both in terms of runtime and memory consumption, particularly on sparse datasets.

Improve the Assessment Model of Personnel Develop Level in Higher Education Based on Machine Learning

In order to improve the ability to manage the quality constraint index parameters of higher education talent training, an evaluation model of higher education talent training quality based on improved machine learning is proposed. Combined with the characteristic analysis method of higher education talent training quality constraint index parameters, an autocorrelation feature matching model of higher education talent training quality constraint index parameters is established, and fuzzy association rule scheduling is used to realize the feature extraction and fuzzy clustering of higher education talent training quality constraint index parameters. The mutual information coupling parameter analysis of the quality constraint index parameters of higher education talent training, combined with the generalized association rules and periodic association rules, realizes the feature clustering and global candidate item set analysis of the quality constraint index parameters of higher education talent training and realizes the dynamic evaluation of the quality of higher education talent training according to the machine learning analysis results. The test results show that this method has good clustering of data characteristics, good reliability of the evaluation process, and good convergence of machine learning process, which improves the dynamic and quantitative management ability of talent training quality in higher education.

A Data Mining Algorithm for Association Rules with Chronic Disease Constraints.

The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China's medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved.