Set Of Data Items Research Articles

Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures

The use of Machine Learning (ML) models as predictive tools has increased dramatically in recent years. However, data-driven systems (such as ML models) exhibit a degree of uncertainty in their predictions. In other words, they could produce unexpectedly erroneous predictions if the uncertainty stemming from the data, choice of model and model parameters is not taken into account. In this paper, we introduce a novel method for quantifying the uncertainty of the performance levels attained by ML classifiers. In particular, we investigate and characterize the uncertainty of model accuracy when classifying out-of-distribution data that are statistically dissimilar from the data employed during training. A main element of this novel Uncertainty Quantification (UQ) method is a measure of the dissimilarity between two datasets. We introduce an innovative family of data dissimilarity measures based on anomaly detection algorithms, namely the Anomaly-based Dataset Dissimilarity (ADD) measures. These dissimilarity measures process feature representations that are derived from the activation values of neural networks when supplied with dataset items. The proposed UQ method for classification performance employs these dissimilarity measures to estimate the classifier accuracy for unseen, out-of-distribution datasets, and to give an uncertainty band for those estimates. A numerical analysis of the efficacy of the UQ method is conducted using standard Artificial Neural Network (ANN) classifiers and public domain datasets. The results obtained generally demonstrate that the amplitude of the uncertainty band associated with the estimated accuracy values tends to increase as the data dissimilarity measure increases. Overall, this research contributes to the verification and run-time performance prediction of systems composed of ML-based elements.

A New Approach for Optimizing the Extraction of Association Rules

Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.

Gaining consensus on clinical quality outcomes for eating disorders: Framework for the development of an Australian national minimum dataset

ObjectivesEating disorders (EDs) are complex psychiatric illnesses requiring multidisciplinary care across both mental and medical healthcare settings. Currently, no nationally comprehensive, consistent, agreed on or mandated data set or data...

Data Provenance in Biomedical Research: Scoping Review.

Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research. The aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption. Following a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures. We identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV. The heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.

Group fairness in non-monotone submodular maximization

Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design fairness-aware algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items.

Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm

For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed, and the efficiency and adaptability of this algorithm on various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets, and make the fully automated deep learning system to ensure the accurate outcome.

AN EVALUATION OF DATA ANONYMIZATION METHODS FOR DATA PUBLISHING

Data anonymization approaches have been the focus of research recently for several types of structured data, including tabular, graph, and item set data. In this article, we provide a succinct yet thorough assessment of a number of anonymization approaches, including generalisation and bucketization, which have been created for publishing microdata while protecting privacy. Recent research has demonstrated that generalisation results in significant information loss, particularly for high-dimensional data. Bucketization, however, does not stop membership disclosure. While slicing both prevents membership disclosure and preserves the data’s superior utility than generalisation. The practical methods that can be employed to provide improved data utility and handle high-dimensional data are the main emphasis of this research.

Multisample-Based Contrastive Loss for Top-K Recommendation

Top-k recommendation is a fundamental task in recommendation systems that is generally learned by comparing positive and negative pairs. The contrastive loss (CL) is the key in contrastive learning that has recently received more attention, and we find that it is well suited for top-k recommendations. However, CL is problematic because it treats the importance of the positive and negative samples the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, there are so few positive items in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. Consequently, we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a multisample-based contrastive loss (MSCL) function that solves the two problems by balancing the importance of positive and negative samples and data augmentation. Based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/haotangxjtu/MSCL</uri> .

Measuring family wealth among secondary school students with home possessions indicators: Investigation of dimensionality and measurement problems

This article investigates methodological problems of measuring family wealth among secondary school students with home possessions indicators. It employs multiple correspondence analysis (MCA) in order to explore (1) the dimensionality of home possessions items, (2) the nature of relationship between these items, and (3) the substantive meaning of the first and other relevant dimensions. It also employs regression analysis in order to study potential explanatory factors of the second dimension of the MCA solutions that may be related to measurement problems of the instrument. Two sources of data are used: (1) open access data from the National Survey of Student Achievement (NSSA) in Lithuania that provide a series of national educational studies conducted from 2002 to 2016, and (2) data from the Programme for International Student Assessment (PISA) study conducted in Lithuania in 2018 that is used to corroborate findings produced using the first data source. Results of the data analysis show that the first dimension of MCA solutions obtained from home possessions items in both NSSA and PISA data sets reflect high vs. low socio-economic status (SES) of the students. However, there is at least one more important dimension, and it reveals multi-dimensional nature of the measurement instrument. Analysis of potential explanatory factors of the second dimension of the MCA solutions revealed that reading abilities and school-related affect is related to this dimension. Thus, the second dimension of MCA solutions might also reflect methodological problems related to the measurement of home possessions in adolescent surveys.

International benchmarking of childhood cancer survival by stage at diagnosis: The BENCHISTA project protocol.

Several studies have shown significant variation in overall survival rates from childhood cancer between countries, using population-based cancer registry (PBCR) data for all cancers combined and for many individual tumour types among children. Without accurate and comparable data on Tumour stage at diagnosis, it is difficult to define the reasons for these survival differences. This is because measurement systems designed for adult cancers do not apply to children's cancers and cancer registries often hold limited information on paediatric tumour stage and the data sources used to define it. The BENCHISTA project aims to test the application of the international consensus "Toronto Staging Guidelines" (TG) for paediatric tumours by European and non-European PBCRs for six common paediatric solid tumours so that reliable comparisons of stage at diagnosis and survival rates by stage can be made to understand any differences. A secondary aim is to test the data availability and completeness of collection of several 'Toronto' consensus non-stage prognostic factors, treatment types given, occurrence of relapse/progression and cause of death as a descriptive feasibility study. PBCRs will use their permitted data access channels to apply the Toronto staging guidelines to all incident cases of six solid childhood cancers (medulloblastoma, osteosarcoma, Ewings sarcoma, rhabdomyosarcoma, neuroblastoma and Wilms tumour) diagnosed in a consecutive three-year period within 2014-2017 in their population. Each registry will provide a de-identified patient-level dataset including tumour stage at diagnosis, with only the contributing registry holding the information that would be needed to re-identify the patients. Where available to the registry, patient-level data on 'Toronto' non-stage prognostic factors, treatments given and clinical outcomes (relapse/progression/cause of death) will be included. More than 60 PBCRs have been involved in defining the patient-level dataset items and intend to participate by contributing their population-level data. Tumour-specific on-line training workshops with clinical experts are available to cancer registry staff to assist them in applying the Toronto staging guidelines in a consistent manner. There is also a project-specific help desk for discussion of difficult cases and promotion of the CanStaging online tools, developed through the International Association of Cancer Registries, to further ensure standardisation of data collection. Country-specific stage distribution and observed survival by stage at diagnosis will be calculated for each tumour type to compare survival between countries or large geographical regions. This study will be promote and enhance the collection of standardized staging data for childhood cancer by European and non-European population-based cancer registries. Therefore, this project can be seen as a feasibility project of widespread use of Toronto Staging at a population-level by cancer registries, specifying the data sources used and testing how well standardized the processes can be. Variation in tumour stage distribution could be due to real differences, to different diagnostic practices between countries and/or to variability in how cancer registries assign Toronto stage. This work also aims to strengthen working relationships between cancer registries, clinical services and cancer-specific clinical study groups, which is important for improving patient outcomes and stimulating research.

Dataset for reporting of gastrointestinal stromal tumours: recommendations from the International Collaboration on Cancer Reporting (ICCR).

Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal tumours of the gastrointestinal tract and are among the most frequent sarcomas. Accurate diagnosis, classification, and reporting are critical for prognostication and patient management, including selection of appropriate targeted therapy. Here we report on international consensus-based datasets for the pathology reporting of biopsy and resection specimens of GIST. The datasets were produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of major international pathology and cancer organizations. An international expert panel consisting of pathologists, a surgical oncologist, and a medical oncologist produced a set of core and noncore data items for biopsy and resection specimens based on a critical review and discussion of current evidence. All professionals involved were subspecialized soft tissue tumour experts and affiliated with tertiary referral centres. Commentary was provided for each data item to explain its clinical relevance and the rationale for selection as a core or noncore element. Following international public consultation, the datasets, which include synoptic reporting guides, were finalized and ratified, and published on the ICCR website. These first international datasets for GIST are intended to promote high-quality, standardised pathology reporting. Their widespread adoption will improve consistency of reporting, facilitate multidisciplinary communication, and enhance comparability of data, all of which will ultimately help to improve the management of patients with GIST. All the ICCR datasets, including these on GIST, are freely available worldwideon the ICCR website (www.iccr-cancer.org/datasets).

Research on a quantitative trading strategy based on high-frequency trading

This article aims to develop a quantitative trading strategy that maximizes profits while finding the best balance of risk and return. We built a high-frequency trading strategy model to maximize profits. We first used the Apriori algorithm to find frequency item sets in historical data before fitting the best daily dynamic position adjustment functions for gold and bitcoin using mathematical statistics and other methods based on price movements. Then we can trade to increase and decrease positions in gold and bitcoin based on the positions suggested by the dynamic position adjustment function. We also simulated three investors with different risk preferences trading using this high-frequency trading model for up to five years and obtained return of 266.05 %, 152.51 %, and 33.29 %, respectively.

Item Layout for Building Materials Management with Apriori Algorithm

The initial stage of opening a business is one of the business activities that can have an impact on the economy. TB. Jasa Manis is a trading business that sells various kinds of building materials, electrical equipment, materials and others. A sales strategy is needed to maintain business and marketing. Data Mining answers this problem by analyzing customer transaction data, then creating a certain rule, pattern or model. Products that are frequently purchased by customers will be used as a reference point in viewing consumer spending patterns. The association technique that can be used is an a priori algorithm designed to find item combinations. The a priori algorithm is guided by the min support and min confidence values. The combination pattern of sales transaction data itemset for two months on TB. Jasa Manis, it can be found that with a combination pattern the minimum support value is 14% and the minimum confidence is 40%. The rules for the highest association or goods that are often purchased together by customers are found in the Lotus Geese/LBR and Roof Nails. Through the analysis of customer shopping habits obtained, the layout of building materials can be adjusted to the customer's shopping pattern. With the data mining socialization rules, patterns of consumer shopping behavior can be identified and can increase sales at TB. Sweet Service

Development and implementation of a national online application system for cross-jurisdictional linked data

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia’s approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

Penerapan Algoritma Apriori Pada Analisa Data Penjualan Ecommerce

Already all sectors are now using the role of this technology. The role of technology in the business sector is to carry out online transactions that can be done anytime and anywhere or commonly referred to as E-commerce. Now E-commerce has grown very rapidly, this is because there are many benefits that can be obtained from the use of E-commerce. But that does not mean that the use of e-commerce is not experiencing losses, the use of e-commerce that is not on target will also create losses for business people. Of course this is a problem that must be solved wisely by being able to read the pattern of goods requested by consumers or products that are not in demand by consumers. Where to find out the pattern can be seen from the e-commerce sales data that is already available. Data mining is a technique used to perform data processing. Data processing is carried out in data mining to obtain new information that can be implemented. A priori algorithm is a method used to find a pattern of relationships between one or more dataset items. In the results of the study, it was found that there are 3 items of E-commerce sales data that can be prioritized for sales. Where of the three items there are 3 combinations of items A1èA2 with a support value of 40% and a confidence value of 67%. A combination of items A1èA5 with a support value of 50% and a confidence value of 83%. Combination of items A2èA5 with a minimum support value of 40% and a confidence value of 50%

Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression

Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification are required to make these data broadly available. Disassociation is a popular method for transactional data anonymization against re-identification attacks in privacy-preserving data publishing. The anonymization algorithm of disassociation is performed in parallel, suitable for the asymmetric paralleled data process in IoT where the nodes have limited computation power and storage space. However, the anonymization algorithm of disassociation is based on the global recoding mode to achieve transactional data km -anonymization, which leads to a loss of combinations of items in transactional datasets, thus decreasing the data quality of the published transactions. To address the issue, we propose a novel vertical partition strategy in this paper. By employing local suppression and global partition, we first eliminate the itemsets which violate km-anonymity to construct the first km-anonymous record chunk. Then, by the processes of itemset creating and reducing, we recombine the globally partitioned items from the first record chunk to construct remaining km-anonymous record chunks. The experiments illustrate that our scheme can retain more association between items in the dataset, which improves the utility of published data.

A cross-cultural mixed methods validation study of the spinal cord injury quality of life basic dataset (SCI QoL-BDS).

Mixed methods inquiry using cognitive interviews and thematic content analysis. Cross-validation of the concept of quality of life (QoL) and of the International Spinal Cord Injury Quality of Life Basic DataSet (SCI QoL-BDS) items across five sites in four countries: United States, Australia, Brazil, and the Netherlands. Analysis aimed to uncover patterns, differences, and similarities suggesting conceptual equivalence for overall QoL and the three SCI QoL-BDS items. International, community. Semi-structured cognitive interviews with 51 participants across five sites and four countries. Participants with spinal cord injury/disease (SCI/D) completed the SCI QoL-BDS items and one additional question. Interviews were audio recorded and transcribed. Transcripts were coded using NVivo software. Coded data were analyzed using thematic content analysis. Seventeen themes were identified. Responses by sites were compared for conceptual equivalence. Across the five sites, equivalence in the conceptual meaning of QoL was found based on the frequent commonalities in terminology employed to describe it. Despite sample differences in terms of demographic and SCI characteristics, participants across all sites replied to the SCI QoL-BDS items in a similar way, suggesting good item equivalence. Qualitatively, the differences noted with respect to the use of themes for each question suggest some variability on how participants with SCI/D describe QoL. In spite of these contextual differences, there is a high degree of commonalty not explained by participants' demographic or injury/disease characteristics. The SCI QoL-BDS shows good cross-cultural validity among the international sites included in this study.

Development and implementation of a national online application system for cross-jurisdictional linked data.

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions

With the rapid development of Internet technology, millions of small, medium, and microenterprises are using Internet recruitment platforms to host their recruitment information. They have different job requirements and benefits positions. It is important to understand them for job seekers when choosing a position. Existing Internet recruitment platforms do not provide a detailed analysis of positions and visual methods for multidimensional matching of positions and job applicants. Candidates need to spend a lot of energy to screen out suitable positions. In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. First, we extract the keywords of the job seeker’s ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. After that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table. We further explore the relationship between the skills required for the three types of positions based on the association rule. Finally, we use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. Simulation results show that our method has better performance on the job analysis and recommendation.

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.