Frequent Item Research Articles

Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and the main challenge has been to achieve a balance between search efficiency and the quality of the mined rules. As a novel swarm intelligence algorithm, the water wave optimization (WWO) algorithm has been widely used for combinatorial optimization problems, with the disadvantage that it tends to fall into local optimum solutions and converges slowly. In this paper, a novel hybrid ARM method based on WWO with Levy flight (LWWO) is proposed. The proposed method improves the solution of WWO by expanding the search space through Levy flight while effectively increasing the search speed. In addition, this paper employs the hybrid strategy to enhance the diversity of the population in order to obtain the global optimal solution. Moreover, the proposed ARM method does not generate frequent items, unlike traditional algorithms (e.g., Apriori), thus reducing the computational overhead and saving memory space, which increases its applicability in real-world business cases. Experiment results show that the performance of the proposed hybrid algorithms is significantly better than that of the WWO and LWWO in terms of quality and number of mined rules.

Computational constraints on the associative recall of spatial scenes.

We consider a model of associative storage and retrieval of compositional memories in an extended cortical network. Our model network is comprised of Potts units, which represent patches of cortex, interacting through long-range connections. The critical assumption is that a memory, for example of a spatial view, is composed of a limited number of items, each of which has a pre-established representation: storing a new memory only involves acquiring the connections, if novel, among the participating items. The model is shown to have a much lower storage capacity than when it stores simple unitary representations. It is also shown that an input from the hippocampus facilitates associative retrieval. When it is absent, it is advantageous to cue rare rather than frequent items. The implications of these results for emerging trends in empirical research are discussed.

Is the population increase of an invasive portunid crab (Charbydis hellerii) a concern? The effect on native prey

Biological invasions have the potential to change the local marine biodiversity through increasing predation pressure and interactions among species. Charybdis hellerii is a swimming crab native to the Indo-West Pacific, it was first collected in the Atlantic Ocean in 1987 and is currently found in high abundance in coastal areas of southeastern Brazil. We evaluated the presence of the invasive crab C. hellerii from the coast of São Paulo state, Brazil, analyzing the stomach contents and performing laboratory-based feeding experiments. In crabs with stomachs full enough to be analyzed (N = 19), mussels (30%) and brachyuran crabs (14.21%) were the most frequent prey items found. Based on these data, we experimentally tested how different densities and life stages (adults and juveniles - differentiated by carapace width - CW) of the invasive crab affected feeding patterns. Results indicated that predator density effects on the predation pressure were dependent on the prey type. Mussels were consumed highest at medium predator densities (30 crabs/m3), whereas crabs were consumed highest at high predator densities (60 crabs/m3). There was no influence of the life stage on prey consumption, suggesting that adults (CW = 57.12–67.59 mm) and juveniles (CW = 39.77–43.18 mm) exhibit similar predation. Charybdis hellerii is an important consumer of benthic native prey, having the potential to change food webs through predation pressure on bioengineers and associated fauna. As the removal of C. hellerii from the environment is difficult, the next step toward is to understand the effect of the invasive crab on the native trophic web.

Assessment of health equity consideration in Cochrane systematic reviews and primary studies on urolithiasis.

Health injustice is defined as "unnecessary, preventable, unjustified and unfair health differences." One of the most important scientific sources on the prevention and management of urolithiasis are Cochrane reviews in this field. Given that the first step in eliminating health injustice is to identify the causes, the aim of the present study was to evaluate equity considerations in Cochrane reviews and the included primary studies on urinary stones. Cochrane reviews on kidney stones and ureteral stones were searched through the Cochrane Library. The included clinical trials in each of the reviews published after 2000 were also collected. Two different researchers reviewed all the included Cochrane reviews and primary studies. The researchers reviewed each PROGRESS criteria independently (P: place of residence, R: race/ethnicity/culture, O: occupation, G: gender, R: religion, E: education, S: socioeconomic status, S: social capital and networks). The geographical location of the included studies was categorized as low-income, middle-income and high-income countries, based on the World Bank income criteria. Each PROGRESS dimension was reported for both the Cochrane reviews and the primary studies. In total, 12 Cochrane reviews and 140 primary studies were included in this study. None of the included Cochrane reviews had specifically mentioned the PROGRESS framework in the Method section whereas gender distribution and place of residence were reported in two and one reviews, respectively. In 134 primary studies at least one item of PROGRESS was reported. The most frequent item was gender distribution, followed by place of residence. According to the results of this study, the authors of Cochrane systematic reviews on urolithiasis, and researchers who have conducted such trials, have rarely considered health equity dimensions when designing and performing their studies. Therefore, researchers worldwide should be motivated to study populations from low-income countries with low socioeconomic status in addition to different cultures, ethnicities, and so forth. Furthermore, RCT reporting guidelines such as CONSORT should include health equity dimensions and the editors and reviewers of scientific journals should encourage researchers to further emphasize on health equity in their studies.

Unravelling the stomach contents of fish and crab species from Cananéia, São Paulo: Are they eating plastic?

Plastic pollution represents a threat to marine ecosystems and has therefore been gaining space in the realm of public interest. In this study, we investigated the ingestion of food and non-food items (i.e., plastic particles) by fish and crabs. These animals are commonly collected by trawling with a double-ring net along the coast of Cananéia, state of São Paulo, Brazil; some of them are consumed as food by the local population. Fish and crab stomachs were removed and dissected, and their contents were examined under a stereoscopic microscope with an image-capturing system. The presence or absence of plastic was also registered. We examined 139 specimens of 16 fish species and 143 specimens of four crab species. The most frequent food items found in fish were unidentified food, followed by crustaceans, molluscs, polychaetes, and other fish; in crabs, the items were unidentified food, followed by crustaceans, molluscs and fish. Plastic particles were found in all fish species, representing 47.5% of the individuals analysed. In crabs, the incidence of plastic was lower, occurring in only two species (5% in Callinectes danae and 3% in C. ornatus). Only four fish species analysed had previous records of plastic ingestion in the scientific literature. The high incidence of microplastics in our study is worrying because they negatively affect the animals’ lives and can be transferred through the tropic web to top predators, including humans, through the ingestion of contaminated animals.

Sorting through life: evaluating patient-important measures of success in a medication for opioid use disorder (MOUD) treatment program

BackgroundMedication for opioid use disorder (MOUD) is the gold standard treatment for opioid use disorder. Traditionally, “success” in MOUD treatment is measured in terms of program retention, adherence to MOUD, and abstinence from opioid and other drug use. While clinically meaningful, these metrics may overlook other aspects of the lives of people with opioid use disorder (OUD) and surprisingly do not reflect the diagnostic criteria for OUD.MethodsAuthors identified items for a pilesorting task to identify participant-driven measures of MOUD treatment success through semi-structured interviews. Interviews were transcribed verbatim and coded in Nvivo using directed and conventional content analysis to identify measures related to treatment success and quality of life goals. Participants of a low-threshold MOUD program were recruited and asked to rank identified measures in order of importance to their own lives. Multidimensional scaling (MDS) compared the similarity of items while non-metric MDS in R specified a two-dimensional solution. Descriptive statistics of participant demographics were generated in SPSS.ResultsSixteen semi-structured interviews were conducted between June and August 2020 in Philadelphia, PA, USA, and 23 measures were identified for a pilesorting activity. These were combined with 6 traditional measures for a total list of 29 items. Data from 28 people were included in pilesorting analysis. Participants identified a combination of traditional and stakeholder-defined recovery goals as highly important, however, we identified discrepancies between the most frequent and highest ranked items within the importance categories. Measures of success for participants in MOUD programs were complex, multi-dimensional, and varied by the individual. However, some key domains such as emotional well-being, decreased drug use, and attendance to basic functioning may have universal importance. The following clusters of importance were identified: emotional well-being, decreased drug use, and human functioning.ConclusionsOutcomes from this research have practical applications for those working to provide services in MOUD programs. Programs can use aspects of these domains to both provide patient-centered care and to evaluate success. Specifics from the pilesorting results may also inform approaches to collaborative goal setting during treatment.

An Efficient Association Rule Mining-Based Spatial Keyword Index

Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are not high when data are skewed. To this end, this paper proposes a novel association rule mining based spatial keyword index, ARM-SQ, whose inverted lists are materialized by the frequent item sets mined by association rules; thus, intersections of long lists can be avoided. To prevent excessive space costs caused by materialization, a depth-based materialization strategy is introduced, which maintains a good balance between query and space costs. To select the right frequent item sets for answering a query, the authors further implement a benefit-based greedy frequent item set selection algorithm, BGF-Selection. The experimental results show that this algorithm significantly outperforms the existing algorithms, and its efficiency can be an order of magnitude higher than SFC-Quad.

MapReduce Framework Based Sequential Association Rule Mining with Deep Learning Enabled Classification in Retail Scenario

Association Rule Mining is used for data mining using the associate rules for detecting frequent items. Hence, this research introduces a novel sequential rule mining for predicting user category using the proposed hybrid Tasmanian Water Devil Optimization based map reduce framework. The introduced Tasmanian Water Devil Optimization integrates the feeding behavior of the Tasmanian devil in the Tasmanian devil optimization with the water cycle process of streams and rivers flowing toward the sea in the water cycle algorithm for choosing the optimal rule from the rule set. In this, the big data based on the retail transaction is utilized for identifying the frequent items using the proposed Tasmanian Water Devil Optimization Sequential Associate Mining technique at the Mapper side. Then, the integration of rules is employed at the Reducer side for the prediction of user category using the Deep Convolutional Neural Network. Thus, the proposed Tasmanian Water Devil Optimization Sequential Associate Mining + Deep Convolutional Neural Network outperformed other conventional methods, obtained enhanced performance in terms of rules mined, execution time, recall, and precision, and obtained the values of 183, 7.70, 0.88, and 0.88, respectively.

Novel Approach to Hide Sensitive Association Rules by Introducing Transaction Affinity

In this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide association rules. The first two algorithms, MaxaffinityDSR and MinaffinityDSR, hide the sensitive information by selecting the victim item as the right-hand side of the sensitive association rule. The next two algorithms, MaxaffinityDSL and MinaffinityDSL, select the victim item from the left-hand side of the rule whereas the Hybrid approach picks the victim item from either the left-hand side or right-hand side. The performance of proposed algorithms has been evaluated by comparison with state-of-art methods (Algo 1.a and Algo 1.b), MinFIA, MaxFIA and Naive algorithms. The experiments were performed using the dataset generated from IBM synthetic data generator, and implementation has been performed in R language.

Bayesian Network Structure Learning by Ensemble Learning and Frequent Item Mining

Aiming at the common problem of low learning effect in single structure learning of a Bayesian network, a new algorithm EF‐BNSL integrating ensemble learning and frequent item mining is proposed. Firstly, the sample set is obtained by sampling the original dataset using Bootstrap, which is mined using the Apriori algorithm to derive the maximum frequent items and association rules so that the black and white list can be determined. Secondly, considering that there may be wrong edges in the black and white list, the black and white list is used as the penalty term of the BDeu score and the initial network is obtained from the hill climbing algorithm. Finally, repeat the above steps 10 times to obtain 10 initial networks. The 10 initial networks were integrated and learned by the integrated strategy function to obtain the final Bayesian network. Experiments were carried out on six standard networks to calculate F1 score and HD. The results show that the EF‐BNSL algorithm can effectively improve F1 score, reduce HD, and learn the network structure that is closer to the real network.

Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system

This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items? cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.

Approaches to parallelise Eclat algorithm and analysing its performance for <i>K</i> length prefix-based equivalence classes

Frequent item set mining (FIM), is one of the prevalent, well-known methods of data mining and is a topic of interest for researchers in the field of decision making. With the establishment of the period of big data where the data is continuously generated from multidimensional sources with enormous volume, and variety in an almost unrevealed way, transforming this data into valuable knowledge discovery which can add value to the organisations to make an efficient decision making poses a challenge in the present research. This leads to the problem of discovery of the maximum frequent patterns in vast datasets and to create a more generalised and interpretable representation of veracity. Targeting the problems stated above, this paper suggests a parallelisation method suitable for any type of parallel environment. The implemented algorithm can be run on a single computer with multi-core processor as well as on a cluster of such machines.

Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review

Data mining has the potential to empower healthcare organizations by allowing them to analyze various aspects of patient information and discover connections between seemingly unrelated data. By harnessing advanced data analysis techniques, healthcare providers can identify trends in patients' medical conditions and behaviours. The Apriori algorithm is used for mining frequent item sets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability, while Apriori-Hybrid. Apriori-Hybrid is the combination of algorithms Apriori and Apriori-TID, which can classify large itemsets and can improve the accuracy of classification and it can also shed light on the basic mechanism. In this research, a comparison was made between the two algorithms in terms of capabilities, strengths, areas of use, and suggestions about the nature of using each algorithm.

An Investigation of Culture Specific Items in English Textbooks Taught at Iraqi State-run High Schools and Private Institutes

Introduction: The issue of culture has been a controversial topic in foreign language teaching. One of the best resources for familiarizing foreign language learners with another language culture is textbook. Therefore, the current study aimed to investigate the culture-specific items in in terms of Kachru’s (1985) concentric circles in English textbooks used in state-run schools and private language institutes in Iraq. 
 Methodology: Two textbooks of New Interchange series and English for Iraq series were analyzed in terms of their culture-specific items using Newmark’s (1988) framework. Cultural items related to categories of ecology, material culture, social culture, organizations, customs and ideas, gestures and habits as well as anthroponyms, were identified, and their frequency counts were computed. The Chi-square test was run to test the significance of each category.
 Results: The results of a Chi-square test indicated that there were no significant differences between New Interchange and English for Iraq series in terms of culture-specific items related to “the expanding circle”. However, a significant difference was found between the two series of textbooks as to the culture-specific items related to “the inner circle”. With regard to cultural items related to the Iraqi native culture, Organizations, Customs, Activities, Procedures, and Concepts were found to be the most frequent items, while Anthroponyms turned out to be the least frequent cultural items reflected in English for Iraq series of textbooks.
 Conclusion: It can be concluded that English textbooks published in English speaking countries (inner circle) and those published in non-native countries, such as Iraq, include items from expanding circle countries.

DIET COMPOSITION, CONDITION FACTOR AND INTESTINAL COEFFICIENT OF THE FISH Astyanax lineatus REFLECT THE ANTHROPOGENIC EFFECTS ON STREAMS IN CENTRAL BRAZIL

Human activities, such as urbanization, can cause instability in the aquatic biota. This instability can modify aquatic food resource availability, consequently shifting the diet composition of fish. Because the diet consumed by fish closely affects the anatomy of their digestive tract, it can also alter their intestinal coefficient. In this study, we analyzed the diet composition, condition factor and intestinal coefficient of Astyanax lineatus (Perugia, 1891) from two distinct habitats: a “degraded habitat” (Cabaça stream) in a reserve inside the city perimeter and a “preserved habitat” (Salobra River) in Mato Grosso do Sul State, Central Brazil. We found a total of 15 food items, all of which were found in the urban stream, with a predominance of detritus and Chironomidae larvae, and six in the natural area, with a predominance of plant remains. There was a distinct difference in diet composition between fishes in the two areas ( p < 0.05, PERMANOVA); however, there was no significant difference in inter-individual variability. The most frequent food items were detritus in the “degraded habitat”, while in the “preserved habitat” plant and insect remains were most frequent. The relative condition factor measured by the fish's length-weight relationship was not significantly different between the two habitats. The intestinal coefficient was significantly different between the individuals from each habitat, and it was longer in fish from the natural habitat. Therefore, even though A. lineatus is known to be a generalist species, its feeding plasticity depends on the ecological integrity of the two types of habitats.

CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates

Frequent itemset mining (FIM) is the crucial task in mining association rules that finds all frequent k-itemsets in the transaction dataset from which all association rules are extracted. In the big-data era, the datasets are huge and rapidly expanding, so adding new transactions as time advances results in periodic changes in correlations and frequent itemsets present in the dataset. Re-mining the updated dataset is impractical and costly. This problem is solved via incremental frequent itemset mining. Numerous researchers view the new transactions as a distinct dataset (partition) that may be mined to obtain all of its frequent item sets. The extracted local frequent itemsets are then combined to create a collection of global candidates, where it is possible to estimate the support count of the combined candidates to avoid re-scanning the dataset. However, these works are hampered by the growth of a huge number of candidates, and the support count estimation is still imprecise. In this paper, the Closed Candidates-based Incremental Frequent Itemset Mining approach, or CC-IFIM, has been proposed to decrease candidate generation and improve the accuracy of the global frequent itemsets that are retrieved. The proposed approach is able to prune several produced candidates in earlier steps before performing any further computations. To improve the accuracy of the computation of the support count of the produced candidates, the similarity between partitions has been evaluated using just the local closed candidates rather than all candidates. The experimental findings demonstrated that the CC-IFIM approach is superior to its competitors in terms of efficiency and accuracy.

Medida da prevalência de intervenção/complicação em puérperas atendidas em hospital universitário durante a pandemia de COVID-19 pelo termômetro de segurança da maternidade

Abstract Objectives: measuring the prevalence of interventions and/or complications based on the Maternity Safety Thermometer (MST) criteria and verifying associations with sociodemographic, clinical, and obstetric factors. Methods: prospective observational study conducted with postpartum women admitted to the maternity ward of a tertiary hospital, from October 10th to December 30th, 2020. Data were collected from medical records and self-administered questionnaires from 260 patients. Results: harm-free care was detected in 17.7% of participants, 66.9% had low-temperature damage (one or less intervention/complication) and 33.1% of patients had elevated temperature damage (two or more intervention/complication). The most frequent intervention was the “scar”, given that 38.5% had abdominal scarring (cesarean section) and 26.5% had perineal scarring (2nd-degree tear or greater – spontaneous or by episiotomy). The second most frequent MST item was related to the perception of safety (30%), followed by complications to the newborn (12.3%), infection (11.2%), and hemorrhage (9.2%). Factors related to high temperature were: being of social class A or B, having a previous cesarean section, and being hospitalized during pregnancy. Conclusions: one-third of the participating women had two or more complications/interventions (high temperature by the MST), factors that are related to this temperature were: being of social class A or B, having a previous cesarean section, and being hospitalized during pregnancy.

How cougars and feral donkeys change desert wetlands: Novel interactions between native predators and non-native megaherbivores shape trophic cascades in the Anthropocene.

Research Highlight: Lundgren, E. J., Ramp, D., Middleton, O. S., Wooster, E. I. F., Kusch, E., Balisi, M., Ripple, W. J., Hasselerharm, C. D., Sanchez, J. N., Mills, M. & Wallach, A. D. (2022) A novel trophic cascade between cougars and feral donkeys shapes desert wetlands. Journal of Animal Ecology, (91, 2010-2022). https://doi.org/10.1111/1365-2656.13766. Despite being absent from most regions of the contemporary world, megafauna species dominated the dynamics of ecological communities until the late Pleistocene. Trophic rewilding is a promising approach to restoring megafauna interactions, their functional roles and the consequent trophic cascades. Unintentional rewilding with large non-native herbivores, such as equids, offers outstanding opportunities for ecologists to understand the outcomes of using replacement species to restore the ecological functions of extinct native megafauna. In this context, it is relevant to understand the extent to which extant native predators can impose top-down control on non-native megaherbivores and how trophic cascades arising from novel predator-prey interactions influences biodiversity and ecosystem functions. In Death Valley National Park (United States), Lundgren et al. depict a natural experiment showing compelling evidence of native cougars overcoming ecological naïveté-that is, the mismatches between predator and prey species that do not share a common evolutionary history-and are now successfully preying on younger individuals of feral donkeys. These non-native donkeys, whose growing wild populations threaten the native biota, became cougars' most frequent dietary item in that region. In areas with cougars, donkeys changed their spatiotemporal foraging patterns, becoming diurnal and less active. On the other hand, donkeys remain more active and forage throughout the day and night in areas without cougars. The cougar-donkey interaction triggered a behaviourally mediated trophic cascade emerging from a 'landscape of fear', that is, from the perception of spatial heterogeneity in predation risk by donkeys. Areas with cougars have less trampled ground, fewer donkey trails, and much more canopy cover and vegetation around water. Donkeys concentrate their activity mostly in topologically plain terrains lacking proper sites for the ambush behaviour of cougars and with more intense human presence, likely acting as a shield against the predators. Lundgren et al. present a promising model system for studying the effects of fearful grazers on community structure in the context of novel ecological interactions being established in the Anthropocene. Whether the emerging cascade is transient or persistent, the relative roles of consumptive and non-consumptive effects as underlying mechanisms, and their consequences for food web structure, functioning and stability, are questions of general interest. Addressing them can help us to elucidate the costs and benefits of using non-native megaherbivores in the functional restoration of permanently invaded ecosystems.

Predicting the Frequent Item Sets for Supermarket Data

This Python notebook uses the Apriori algorithm to analyze datasets from various supermarkets, retail organizations, and minimarkets, resulting in a more accurate analysis of customer behaviour and better product prediction and forecasting. The dataset that is used in this model typically involves customer purchases in supermarkets or any other organization. The datasets contain item details as well as the number of transactions purchased by customers. This model can be used by retailers and supermarkets of all sizes in both urban and rural areas. This algorithm implementation enables accurate forecasting and allows products to be sold efficiently and profitably in stores. Supermarkets, for example, can use the resulting data to forecast future sales volume using a variety of machine-learning techniques. It displays the most frequently purchased items or associated items by the user. This prediction is primarily focused on figuring out the rules of the association. It identifies the set of items or attributes that occur together or frequently in the dataset using association rules. If this apriori model meets a minimum threshold value for support and confidence, it produces a set of items known as a frequent itemset. This Python notebook implements a prediction model based on the apriori algorithm, which improves the efficiency of level-wise generation of frequent item sets by utilizing an important property known as the Apriori property, which aids in reducing the search space.

Locally Differentially Private Frequent Pattern Mining for High-Dimensional Data in Mobile Smart Services

Collecting users’ historical data such as movie watching and music listening, and mining frequent items from them, can improve the utility of smart services, but there is also a risk of compromising user privacy. Local differential privacy is a strict definition of privacy and has been widely used in various privacy-preserving data collection scenarios. However, the accuracy of existing locally differentially private frequent items mining methods decreases significantly with the increase in the dimensions of data to be collected. In this paper, we propose a new locally differentially private frequent item mining method for high-dimensional data, which decreases the dimension used for data perturbation by grouping the contents and improving the interference matrix generation method, so as to improve the data reconstruction accuracy. The experimental results show that our proposed method can significantly improve the accuracy of frequent item mining and provide a better trade-off between privacy and accuracy compared with existing methods.