Large Itemsets Research Articles

Scaling a common assessment of associative ability: Development and validation of a multiple-choice compound remote associates task.

The assessment of creativity as an individual difference has historically focused on divergent thinking, which is increasingly viewed as involving the associative processes that are also understood to be a key component of creative potential. Research on associative processes has proliferated in many sub-fields, often using Compound Remote Associates (CRA) tasks with an open response format and relatively small participant samples. In the present work, we introduce a new format that is more amenable to large-scale data collection in survey designs, and present evidence for the reliability and validity of CRA measures in general using multiple large samples. Study 1 uses a large, representative dataset (N = 1,323,480) to demonstrate strong unidimensionality and internal consistency (α = .97; ωt = .87), as well as links to individual differences in temperament, cognitive ability, occupation, and job characteristics. Study 2 uses an undergraduate sample (N = 685) to validate the use of a multiple-choice format relative to the traditional approach. Study 3 uses a crowdsourced sample (N = 357) to demonstrate high test-retest reliability of the items (r =.74). Finally, Study 4 uses a sample that overlaps with Study 1 (N = 1,502,922) to provide item response theory (IRT) parameters for a large set of high-quality CRA items that use a multiple-choice response mode, thus facilitating their use in future research on creativity, insight, and related topics.

The Cognitive Failures Questionnaire 2.0

Individual differences in the subjective experience of cognitive failures predict important outcomes, such as accident risk. Over the last four decades, these have been measured via the Cognitive Failures Questionnaire (CFQ). However, multiple CFQ items have diminished contemporary relevance and technology-related failures are not represented at all. Further, various attempts to identify multicomponent structures have had problems, which could be due to small numbers of items from which these structures are devised. Here we developed a large set of new items to more fully sample underlying psychological processes, including contemporary instances of cognitive failures, and performed an exploratory factor analysis (EFA) in a sample of adults (Study 1). This identified one dominant factor, which was supported by a subsequent confirmatory factor analysis (CFA) in Study 2. Study 3 replicated the CFA and demonstrated that scores explained variance in objective performance on an attentional control task beyond that explained by the original CFQ. Study 4 demonstrated that scores were associated with focusing, switching, cognitive empathy, affective empathy, and negative affect in ways consistent with the original CFQ. In Study 5, scores demonstrated good test-retest reliability. We offer this 15-item scale as a new and improved measure of cognitive failures (CFQ 2.0).

Data Mining Tools for Generate Item Set : Critical Review

Most algorithms used to identify large itemsets can be classified as either sequential or parallel. In most cases, it is assumed that the itemsets are identified and stored in lexicographic order (based on item name). This ordering provides a logical manner in which itemsets can be generated and counted. This is the normal approach with sequential algorithms. On the other hand, parallel algorithms focus on how to parallelize the task of finding large itemsets. Mining Associations is one of the techniques involved in the process mentioned in chapter 1 and among the data mining problems it might be the most studied ones. Discovering association rules is at the heart of data mining. Mining for association rules between items in large database of sales transactions has been recognized as an important area of database research. These rules can be effectively used to uncover unknown relationships, producing results that can provide a basis for forecasting and decision making. Today, research work on association rules is motivated by an extensive range of application areas, such as banking, manufacturing, health care, and telecommunications. It is also used for building statistical thesaurus from the text databases, finding web access patterns from web log files, and also discovering associated images from huge sized image databases.

A New Approach to Desirable Responding: Multidimensional Item Response Model of Overclaiming Data.

A variety of approaches have been presented for assessing desirable responding in self-report measures. Among them, the overclaiming technique asks respondents to rate their familiarity with a large set of real and nonexistent items (foils). The application of signal detection formulas to the endorsement rates of real items and foils yields indices of (a) knowledge accuracy and (b) knowledge bias. This overclaiming technique reflects both cognitive ability and personality. Here, we develop an alternative measurement model based on multidimensional item response theory (MIRT). We report three studies demonstrating this new model's capacity to analyze overclaiming data. First, a simulation study illustrates that MIRT and signal detection theory yield comparable indices of accuracy and bias-although MIRT provides important additional information. Two empirical examples-one based on mathematical terms and one based on Chinese idioms-are then elaborated. Together, they demonstrate the utility of this new approach for group comparisons and item selection. The implications of this research are illustrated and discussed.

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.

A comparative study of item space visualizations for recommender systems

Recommender systems aim at supporting users in their search and decision making process by selecting a small number of likely relevant items from a large set of options. Although automatically filtering unmanageably large item sets down to a few recommendations often produces results that match the user’s interests well, it also prevents users from understanding and exploring items in their larger context. This may reduce users’ perception of transparency and controllability of the system. Visualizations have been proposed as a means for overcoming this problem, with some visualizations providing a complete overview of the entire space of available items. However, thus far item space visualizations have rarely been investigated and compared in user studies. To address this, we developed and empirically compared three applications that present the user with personalized music recommendations embedded in a visualization of the entire item space. The three applications display the same item space as a list, as a treemap, and as a map, respectively. We compared these applications in an online user study and found, against our expectations, that they did not differ much in how the recommendations are perceived. Perception of transparency, recommendation quality, and degree of control over the recommendations received relatively high scores over all three applications. However, we did find a difference in hedonic user experience and perceived novelty of the recommendations. Both factors were perceived to be higher in the map condition. Backed up by a mediation analysis, we argue that a halo effect is the reason for the observed perceived novelty: participants transferred the novelty of the application to the novelty of the recommendations.

MapReduce Framework to Improve the Efficiency of Large Scale Item Sets in IoT Using Parallel Mining of Representative Patterns in Big Data

With the coming of the time of huge information, individuals can gather rich and various information from a wide assortment of assortment gadgets, like those of the Internet of Things. Information concealed in enormous information is extremely helpful and important. Frequent pattern mining, as a basic method of data mining, is applied to every aspect of society. However, the application of traditional frequent pattern mining methods to big data involves bottlenecks due to the large number of result sets. Such bottlenecks make it challenging to deliver commonsense worth underway and life. We proposed a new approach which involved representative patterns using mining technique. This framework can make the runtime difficult to evaluate in large data environments. Our approach gives best solution for the above gaps with the help of online representative pattern-set parallel-mining algorithm. We used MapReduce framework which performs horizontal segmentation over data bases. Finally, several performance optimization strategies are proposed. As shown by numerous experiments on the actual dataset, the algorithm proposed in this paper improves the time efficiency by one order of magnitude. Several optimization strategies reduce the execution time to varying degrees.

Grouping Goods with the Association Rule Method Using A priori Algorithms in Modern Retail Stores

The proliferation of retail companies in Indonesia makes competition quite fierce in this business. The availability of detailed information on products purchased from each customer transaction in the database will become data garbage, which continues to grow every day. Company managers will certainly be interested in knowing whether certain groups of products are consistently purchased together. They can use this data for optimal grouping and placement of products, and managers can also use this information for cross-selling, for promotions, for catalog design and to identify customer segments based on purchasing patterns. In this study, data mining with Association rule techniques was used to explore patterns of attribute relationships and frequent itemsets in the Retail database. The a priori paradigm is used to find large itemsets in the determination of association rules. The integration of association rules with the a priori paradigm has managed to find a number of patterns of relationships between attributes in retail databases.

BABOONS

BABOONS (BlAck BOx Optimization of Natural language data Summaries) optimizes text data summaries for an arbitrary, user-defined utility function. Primarily, it targets scenarios in which utility is evaluated via large language models. Users describe their utility function in natural language or provide a model, trained to score text summaries in a specific domain.BABOONS uses reinforcement learning to explore the space of possible descriptions. In each iteration, BABOONS generates summaries and evaluates their utility. To reduce data processing overheads during summary generation, BABOONS uses a proactive processing strategy that dynamically merges current with likely future queries for efficient processing. Also, BABOONS supports scenario-specific sampling and batch processing strategies. These mechanisms allow to scale processing to large data and item sets. The experiments show that BABOONS scales significantly better than baselines. Also, they show that summaries generated by BABOONS receive higher average grades from users in a large survey.

A Comparative and Performance Analysis of Similarity Metrics in Recommender System Based on Hadoop Framework

With exponential growth of information available on web, there is a need for a personal assistance to customers for finding the best item out of the large set of items. There is also need for finding the best item having highest popularity. The personal assistance which will assist the user is the Recommender System. It is a software tool that uses knowledge discovery techniques to produce personalized recommendations. The majority of the recommendations are based on the machine learning algorithm and techniques which uses a standard data set for producing predictions. With tremendous growth of customers and items in the recent years, there is a key challenge for the recommendation system to produce quality recommendations based on the similarity of the users. There also lies a great challenge for the recommendation system to produce quality output when data set is huge, reduce latency, group users having similar interest and perform recommendations in seconds for millions of customers and products. Thus there is a need for new recommendation technologies which will produce quality output to users having similar tastes in a best possible manner. In this paper we present a comparative analysis of various similarity metrics used in recommendation system for clustering the users based on hadoop framework. The experimental results show that spearman rank correlation performs the best among the other similarity metrics based on three evaluation metrics.

Model-Based and Graph-Based Priors for Group Testing

The goal of the group testing problem is to identify a set of defective items within a larger set of items, using suitably-designed tests whose outcomes indicate whether any defective item is present. In this paper, we study how the number of tests can be significantly decreased by leveraging the structural dependencies between the items, i.e., by incorporating prior information. To do so, we pursue two different perspectives: (i) As a generalization of the uniform combinatorial prior, we consider the case that the defective set is uniform over a \emph{subset} of all possible sets of a given size, and study how this impacts the information-theoretic limits on the number of tests for approximate recovery; (ii) As a generalization of the i.i.d.~prior, we introduce a new class of priors based on the Ising model, where the associated graph represents interactions between items. We show that this naturally leads to an Integer Quadratic Program decoder, which can be converted to an Integer Linear Program and/or relaxed to a non-integer variant for improved computational complexity, while maintaining strong empirical recovery performance.

DWORM: A Novel Algorithm To Maintain Large Itemsets in Deleted Items and/or Transactions Databases Without Re-Mining

Transactional databases can be updated by three cases; the addition of new transactions, the deletion of set of transactions, andor increasing or decreasing of the support of the itemsets. The update process affects the previously mined itemsets, some of the large items will be small and vice versa. Therefore the updated database should be re-mined to discover the changes in the hidden itemsets. There are many algorithms to avoid the re-mining process in the case of updating a database by addition and there is one algorithm in case of changing the value of the support. But there is no actual algorithm to avoid the re-mining in the case of deletion. This research presents a novel algorithm to manipulate this case. The proposed algorithm manipulates the three possibilities of the deletion that are deletion of one or more items from a transaction or set of transactions, deletion of a set of transactions, and deletion of items from transactions and deletion of set of transactions at the same time. The experimental result shows that the updating algorithm outperforms the re-mining process in considerable amount of execution time.

Grouping strategies in numerosity perception between intrinsic and extrinsic grouping cues

The number of items in an array can be quickly and accurately estimated by dividing the array into subgroups, in a strategy termed “groupitizing.” For example, when memorizing a telephone number, it is better to do so by divide the number into several segments. Different forms of visual grouping can affect the precision of the enumeration of a large set of items. Previous studies have found that when groupitizing, enumeration precision is improved by grouping arrays using visual proximity and color similarity. Based on Gestalt theory, Palmer (Cognit Psychol 24:436, 1992) divided perceptual grouping into intrinsic (e.g., proximity, similarity) and extrinsic (e.g., connectedness, common region) principles. Studies have investigated groupitizing effects on intrinsic grouping. However, to the best of our knowledge, no study has explored groupitizing effects for extrinsic grouping cues. Therefore, this study explored whether extrinsic grouping cues differed from intrinsic grouping cues for groupitizing effects in numerosity perception. The results showed that both extrinsic and intrinsic grouping cues improved enumeration precision. However, extrinsic grouping was more accurate in terms of the sensory precision of the numerosity perception.

Burnout: Redefining its key symptoms

Burnout is currently operationalised as comprising of exhaustion, depersonalization and reduced personal accomplishment. However, questions have been raised as to whether this three-factor model accurately characterises the syndrome. We therefore sought to re-examine burnout's key symptoms and to develop a new model of the syndrome. 622 participants who self-identified as experiencing burnout completed a questionnaire covering a large item set of candidate burnout symptoms. Bifactor modelling identified a 34-item general factor that was dominated by items capturing exhaustion and cognitive dysfunction, but which also included items indicative of decreased work performance, insularity and a depressed mood. Five specific factors capturing additional data variance were identified and were interpreted as representing cognitive impairment, empathy loss, exhaustion, compromised work performance, and social withdrawal. Reliability indices indicated that the general factor alone accounted for most of the variance in observed scale scores for each specific factor, except for the loss of empathy specific factor. We concluded that burnout may therefore potentially be modelled as a unidimensional construct comprising exhaustion, cognitive impairment, compromised work performance, empathy loss and social withdrawal. Further, while those with burnout are likely to experience depressive symptoms, their presence is not of necessity indicative of clinical depression.

A two-step, test-guided Mokken scale analysis, for nonclustered and clustered data

PurposeMokken scale analysis (MSA) is an attractive scaling procedure for ordinal data. MSA is frequently used in health-related quality of life research. Two of MSA's prime features are the scalability coefficients and the automated item selection procedure (AISP). The AISP partitions a (large) set of items into scales based on the observed item scores; the resulting scales can be used as measurement instruments. There exist two issues in MSA: First, point estimates, standard errors, and test statistics for scalability coefficients are inappropriate for clustered item scores, which are omnipresent in quality of life research data. Second, the AISP insufficiently takes sampling fluctuation of Mokken’s scalability coefficients into account.MethodsWe solved both issues by providing point estimates and standard errors for the scalability coefficients for clustered data and by implementing a Wald-based significance test in the AISP algorithm, resulting in a test-guided AISP (T-AISP), that is available for both nonclustered and clustered test scores.ResultsWe integrated the T-AISP into a two-step, test-guided MSA for scale construction, to guide the analysis for nonclustered and clustered data. The first step is performing a T-AISP and select the final scale(s). For clustered data, within-group dependency is investigated on the final scale(s). In the second step, the strength of the scale(s) is determined and further analyses are performed. The procedure was demonstrated on clustered item scores obtained from administering a questionnaire on quality of life in schools to 639 students nested in 30 classrooms.ConclusionsWe developed a two-step, test-guided MSA for scale construction that takes into account sample fluctuation of all scalability coefficients and that can be applied to item scores obtained by a nonclustered or clustered sampling design.

The effects of audiovisual landmarks on spatial learning and recalling for image browsing interface in virtual environments

To allow more accurate and efficient browsing performance, existing works showed that visual landmarks (e.g.3D pins) can facilitate spatial learning and recalling of browsing performance in Virtual Reality (VR). Compared with visual cues, the additional auditory cue is well known to provide benefits of navigational performance in virtual environments. However, less is known about the effects of such audiovisual landmarks on facilitating spatial learning and recalling for VR browsing interfaces. In this work, we followed and extended our previous work to further investigate this study (no landmark (N), 3D visual pin (V)), and newly implemented another two types of landmark interfaces, including spatial auditory landmark (S) and spatial audiovisual landmark (SV) in VR. We examined their effects on facilitating learning and recalling items’ locations in small and large item sets in these four interfaces. We found that three landmark interfaces outperform no landmark in terms efficiency, and the participants performed the best with audiovisual landmark interface, however, no significant difference of retrieval and recall performances between the visual and the audio interfaces. The findings provide the suggestion of enabling audiovisual landmarks for VR browsing interfaces.

Access to after-hours primary care: a key determinant of children\u2019s medical home status

BackgroundThe medical home (MH) model has been promoted by both the federal and state governments in the United States in recent years. To ascertain American children’s MH status, many studies have relied on a large set of survey items, posing a considerable burden on their parents. We aimed to identify individual survey items or domains that best predict MH status for children and use them to develop brief markers of MH status. We also examined whether the identified items differed by status of special health care needs and by racial/ethnic group.MethodUsing the 9-year data from Medical Expenditure Panel Survey, we examined associations between children’s MH status and individual survey items or domains. We randomly split the data into two halves with the first half (training sample, n = 8611) used to identify promising items, and the second half (validation sample, n = 8779) used to calculate all statistical measures. After discovering significant predictors of children’s MH status, we incorporated them into several brief markers of MH status. We also conducted stratified analyses by status of special health care needs and by racial/ethnic group.ResultsLess than half (48.7%) of the 8779 study children had a MH. The accessibility domain has stronger association with children’s MH status (specificity = 0.84, sensitivity = 1, Kappa = 0.83) than other domains. The top two items with the strongest association with MH status asked about after-hours primary care access, including doctors’ office hours at night or on the weekend and children’s difficulty accessing care after hours. Both belong to the accessibility domain and are one of several reliable markers for children’s MH status. While each of the two items did not differ significantly by status of special health care needs, there were considerable disparities across racial/ethnic groups with Latino children lagging behind other children.ConclusionAccessibility, especially the ability to access health care after regular office hours, appears to be the major predictor of having a MH among children. The ongoing efforts to promote the MH model need to target improving accessibility of health care after regular hours for children overall and especially for Latino children.

A Yardstick for Danger: Developing a Flexible and Sensitive Measure of Risk Perception.

While individual perceptions of risk are central to many behavioral theories of hazard response and are of considerable interest in both conceptual and applied work surrounding risk, hazards, and decision making, there is currently no consensus on how perceived risk should best be measured. Several recent efforts have laid the groundwork for a conceptual model outlining four key factors that make up risk perception: exposure, susceptibility, severity, and affective response. In this article, we use an extensive scale-development process to develop empirically supported 3-4 item subscales to measure each of those four dimensions. Using cognitive interviewing techniques and several quantitative psychometric methods including exploratory and confirmatory factor analysis and item-response theory analyses, we reduce a large set of potential items to the highest-quality items to assess each subscale. These subscales can be used to make comparisons across perceived risk in different hazard contexts and populations.

To Improve the Web Personalization Using the Boosted Random Forest for Web Information Extraction

Background: Web personalization is kind of method that is applied to modify a web site to suit the exact needs of the users, achieving the advantage of data accomplished for the understanding the directional conduct of users concerning inclusion of more materials in the web framework. Methods: In this paper an Finding Large Itemsets produce all blends of things that comprises of a bolstering an incentive over a client illustrated least support. The total number of exchanges which consists of the itemset is nothing but the support for an itemset. For the purpose of ranking the list, the calculated values with the user ranked list are offered to the fuzzy-bat. Results: The result shows that the proposed methodology that perceives the challenge of mining affiliation presides over in an organization of exchanges could be defined as the problem of producing all the affiliation henceforth making a decision which would possess bolster esteem more significant in comparison to a client that exemplified least support as well as certainty esteem more noteworthy than a client characterized least certainty. Conclusion: This paper comprehensively describes the our proposed the preciseness of the system is in contrast to a great extent with the present method for the varied questions that has been provided. The computed esteems with the client positioned rundown are provided to the fluffy bat to rank the rundown. The proposed technique has been compared with the prevailing fuzzy technique with regard to the response time as well as the precision.

Neural text similarity of user reviews for improving collaborative filtering recommender systems

Abstract According to the advent of technology and the expansion of using the World Wide Web, there has been an enormous increase in the number of Internet retailers and their customers. The amount of data is exploding due to the numerous users and the variety of products. In such a condition, recommender systems play an important role in helping users and providing suggestions based on their taste. Using recommender systems’ suggestions, users can save their time on finding their personalized, useful, and favorite items without being overwhelmed by a large set of items. User-based collaborative filtering recommender systems for each user find similar users based on their ratings and suggest their favorite products to the intended user. In this paper, we present a model to improve recommender systems by finding similar users based on their reviews in addition to their ratings. To this end, we compute users’ reviews similarity utilizing seven different approaches, out of which two techniques are lexical-based, two techniques benefit from the neural representation of words, and three techniques are based on the neural representation of texts. Our experiments on two different datasets from Amazon show that the proposed model for capturing review’s similarity significantly improved the performance of the recommender system. Moreover, the model based on Long Short Term Memory (LSTM) network achieves the best results.