Interrelated Items Research Articles

Analisis Pengambilan Keputusan Untuk Korelasi Pembelian Produk Menggunakan Metode Association Rules

Business people are required to be able to keep up with the increasingly dynamic times. Business people must have the right strategy to be able to compete with competitors so that the business they run continues to generate profits. From the existing sales transaction data, it can be used for decision making for business people in developing a good strategy in developing their business. By using data mining with the Association Rules method can be used to analyze existing sales patterns. In this test, the author uses the Apriori Algorithm to analyze the market basket analysis to find out the correlation between product purchases and products that are often purchased by consumers. From the analysis of the a priori algorithm, it produces frequent items that meet the minimum values ??of support and confidence. The result of this test is to provide product recommendations to consumers based on interrelated items and as a reference in product promotion to support decisions in developing good strategies in their business.

A Theoretical Examination of the Composite Index or Measuring a Variable Using Likert Scale

The assumption whilst using Likert scales is that the intensity or strength of character and personality traits such as attitudes can be measured on a linear continuum from strongly agree to strongly disagree. This paper discusses Likert scale items vis-a-vis Likert type items and highlights the difference between the two. Likert scale used interrelated items that can be converted into a composite score during data analysis. As such appropriate inferential statistical techniques can be applied. Conversely Likert items are individual items based on an ordinal scale and data analysis is specific to the item and only non-parametric statistical methods are appropriate.

Prediction of material requirements for network construction using apriori algorithm

PT XYZ is one of the providers of fixed broadband development services spread throughout Indonesia. What often happens is the lack of material availability and the long pre-order process, thus hampering projects that have been agreed upon for completion. The a priori algorithm is one of the data mining algorithms in the association method, which is looking for relationships between interrelated items. This research uses the help of RStudio software. This prediction is expected to help to prepare material stock in the warehouse so as to prevent material vacancies in the warehouse. The results of this study are in the form of a rule that meets the minimum support value (a measure that shows how much dominance an item/item set is from the entire transaction) of 27.84%, minimum confident (a measure that shows the relationship between 2 items conditionally) of 27.84%. 84.48% and the lift ratio (a measure to see whether or not the association rules are formed) is > 1.

Compatibility Improvement of Interrelated Items in Exchange Files—A General Method for Supporting the Data Integrity of Digital Twins

Stakeholders in the industry are increasingly using digital twins to take advantage of continuous digitization. The widely used methods for transferring partial models of digital twins within various heterogeneous systems rely on standardized, neutral file-based exchange. However, using differently implemented routines in the pre- and postprocessors of the systems engaged during data transmission leads to compatibility problems. Complete information transfer is not guaranteed, although potentially all information is available in the individual exchange file. To utilize the full potential of digital twins, this paper presents a method for directly adapting the content stored in an exchange file to systematically achieve compatibility. In the first step, we define a general structure to specify interrelated, nonconforming objects that are stored in the exchange file. We present five conditions that specify a compatibility problem in the following steps. On this basis, the applicant can solve various exchange problems for the indicated scenario in the third step. After explaining the approach in general terms, we demonstrate its generality by discussing two diverging use cases based on the exchange formats STEP and INP. We implemented the method in software terms, and the implementation indicates that this method can fix compatibility problems in an automated way.

Evolutionary behaviour of ‘inflating’ random real matrices for economy or biology: stasis statistics of vector iterations upon growth

A scheme is proposed for describing stasis and transitions in evolutionary contexts defined by a growing number of interrelated items. These items could be genes/species in biology, or tools/products in economy. The target is a frame to describe the advent of stasis marked by dominant species or dominant objects (car, smartphone) between sharp transitions (quakes). The tool of random matrices is adapted to add an explicit varietal growth, through an ‘inflation’ of a real random matrix (Ginibre set), by regularly adding a line and a column, such a matrix operating at each unit time step on the evolving vector, U(t + 1) = M(t)U(t). In this view, U(t) ∝ log(C(t)) with C(t) the vector of abundances of genes in a gene pool, or of abundance of tools in a multi-sector production economy (as in Leontieff matrices of sector-wise productions). U(t) is trending toward the eigenvector with the largest-modulus eigenvalue (ev) U (N) for the current N(t). Most times, the next such eigenvector U (N+1) of the inflated matrix is close and mostly colinear to U (N). But, as time goes and N also grows, a wholly unrelated eigenvector may acquire a larger modulus ev and become the new attractor. Thus, there are slowly-moving stases punctuated by quakes. The leading-ev-modulus competition is elucidated, as well as the main features of the stasis duration distribution it entails, akin to a q-exponential law. This is done by means of a two-particles model of Brownian motion taking place with an N-dependent drift and diffusion. To minimally flesh the issue, a set of bibliographic data (yearly number of papers among all journals of a domain) is used, lending support to the vector-colinearity-based methods used for the detection of stases vs quakes. Hints are given for various developments tackling the appropriateness of the model to various growth contexts, e.g. with sparse network or with death and not only novelty/birth.

Streamlining Universal Prenatal Screening for Risk for Adverse Birth Outcomes.

Many of the medical risk factors for adverse birth outcomes (e.g., preeclampsia) are regularly monitored in prenatal care. However, many of the psychosocial risk factors associated with adverse birth outcomes (e.g., maternal stress, anxiety, depression, intimate partner violence) are not regularly addressed during routine prenatal care. Comprehensive prenatal screening for psychosocial risk factors for adverse birth outcomes can improve maternal and neonatal outcomes. In this study, we examine an existing tool for opportunities to streamline and improve screening. We reviewed medical records for 528 mother-infant dyads, recording maternal responses to a 21-item prenatal risk screening tool, and gestational age/birth weight of infants. Multiple approaches to scoring were used to predict likelihood of adverse birth outcome. Women who answered yes to any of the top four interrelated items were 3.32 times more likely to have an adverse birth outcome. Sensitivity and specificity were 68% and 65%, respectively. We identified a short surveillance tool to identify women who are at highest risk and require more in-depth screening, and to rule out women who are at very low risk of an adverse birth outcome.

Comparison of Apriori and Frequent Pattern Growth Algorithm in Predicting The Sales of Goods

The increasing number of bona fide companies, especially in the world of retail minimarkets, PT. Suka Maju innovates to make a company that develops in the retail sector so that it can serve consumers well. With the problems - problems in the company PT. Suka Maju still applies unrelated items so that consumers find it difficult to buy related products. PT. Suka Maju does not apply interrelated items such as coffee and sugar, sauce and noodles, bread and cheese. company PT. Suka Maju must act as quickly as possible and requires data analysis using Market Basket Analysis. The purpose of the existence of data in every transaction of product sales to consumers, data can be processed properly to provide information to companies so that transaction data in every product purchase can be useful and to determine the layout of a product. To deal with this problem, researchers found a pattern that can improve a layout pattern or display of sales items in the retail world, one of which is by utilizing product sales transaction data used to support and find an association rule data mining method technique, comparing the algorithm Apriori and algorithm Frequent Pattern Growth. The purpose of this study is to compare 2 algorithms and choose a better algorithm to help find products that are often purchased together. From the results of the research from 10,005 transactions of 27 attributes using the algorithms Apriori and algorithms Frequent Pattern Growth with the minimum parameters of support = 100, confidence = 100 and lift = 2.58, the algorithm Frequent Pattern Growth has the highest accuracy compared to the algorithm Apriori. In the results of this study, it can be said that the algorithm Frequent Pattern Growth is the best for determining interrelated

Tradurre l’impossibile in immagine. Funambolismo e (fanta)scienza nel cinema di Werner Herzog

This paper investigates the deep relationship between the films of German filmmaker Werner Herzog and the conceptual category of the impossible, especially the cinematic representation of something which can be defined as “unrepresentable”. The analysis focuses on the stylistic modes that contributed to making Herzog’s cinema recognizable, with attention to the following interrelated items:1) The cinematographic technique of the “ecstatic truth” (Prager, 2007);2) The conceptualization of the gaze and the vision within the filmic text through stories and settings linked to the motifs of the challenge and the titanic enterprise (Grosoli and Reiter, 2000; 2016).Thanks to the formal analysis of a corpus of fiction and documentary films, this paper tries to define funambulism and science in Herzog's filmography as instruments which convey on screen and in the viewer’s gaze stories, environments and landscapes that are poetically linked to the motif of the impossible.

Analysis of the performance of competing models for aggregate demand forecasting using observable data characteristics

This paper provides an analysis of the performance of two simplified approaches for forecasting the aggregate demand for a product family consisting of two interrelated items. The correlated demand characteristic of the product family is modelled by bivariate vector IMA(1, 1) process. The forecasting approaches chosen for evaluation are univariate scheme and aggregate scheme. The performances of these methods are analysed by theoretically determining the forecast mean square error (MSE) in terms of observable data characteristics. Further, subsequent evaluation of percentage changes of these MSEs with respect to the MSE of original bivariate model has been conducted to determine how far the forecast MSE values deviate from the original model. Through numerical experimentation, the necessary and sufficient conditions in terms of observable data characteristics are determined for the two approaches to be equal in terms of forecast MSE of the original bivariate model.

Extending Cognitive Work Analysis for embodiment: ecological psychology, activity theory and Worker Competency Analysis

ABSTRACTWith the growth of novel embedded technologies, human interaction with ‘smart’ environments will require a comprehensive conceptualisation of the body and embodied cognition. Thus, producing a requirement for work analysis approaches to address the embodied aspects of work systems. Correspondingly, this article presents two interrelated items. First, it extends Cognitive Work Analysis (CWA) on the basis of embodiment, ecological psychology and activity theory. The embodied approach to cognition emphasises that human knowing (cognition) is shaped by the body and behaviour: the body, mind and activity are intricately entwined. In extending CWA for the embodiment, this article presents the second contribution in terms of the ‘embodiment hierarchy’ in the fifth phase of CWA – Worker Competency Analysis, for addressing the constraints related to embodiment. An example in the nanotechnology domain is provided to demonstrate the above-mentioned concepts.

Improving operating room schedules.

Operating rooms (ORs) in US hospitals are costly to staff, generate about 70 % of a hospital's revenues, and operate at a staffed-capacity utilization of 60-70 %. Many hospitals allocate blocks of OR time to individual or groups of surgeons as guaranteed allocation, who book surgeries one at a time in their blocks. The booking procedure frequently results in unused time between surgeries. Realizing that this presents an opportunity to improve OR utilization, hospitals manually reschedule surgery start times one or two days before each day of surgical operations. The purpose of rescheduling is to decrease OR staffing costs, which are determined by the number of concurrently staffed ORs. We formulate the rescheduling problem as a variant of the bin-packing problem with interrelated items, which are the surgeries performed by the same surgeon. We develop a lower bound (LB) construction algorithm and prove that the LB is at least (2/3) of the optimal staffing cost. A key feature of our approach is that we allow hospitals to have two shift lengths. Our analytical results form the basis of a branch-and-bound algorithm, which we test on data obtained from three hospitals. Experiments show that rescheduling saves significant staffing costs.

Improving Operating Room Schedules

Operating rooms (ORs) in US hospitals are costly to staff, generate about 70% of a hospital’s revenues, and operate at a staffed-capacity utilization of 60-70%. Many hospitals allocate blocks of OR time to individual or groups of surgeons as guaranteed allocation, who book surgeries one at a time in their blocks. The booking procedure frequently results in unused time between surgeries. Realizing that this presents an opportunity to improve OR utilization, hospitals manually reschedule surgery start times one or two days before each day of surgical operations. The purpose of rescheduling is to decrease OR staffing costs, which are determined by the number of concurrently staffed ORs. We formulate the rescheduling problem as a variant of the bin-packing problem with interrelated items, which are the surgeries performed by the same surgeon. We develop a lower bound (LB) construction algorithm and prove that the LB is at least (2/3) of the optimal staffing cost. A key feature of our approach is that we allow hospitals to have two shift lengths. Our analytical results form the basis of a branch-and-bound algorithm, which we test on data obtained from three hospitals. Experiments show that our approach can lead to substantial improvements over current methods used by hospitals to reschedule surgeries.

Organizing Search Results with a Reference Map.

We propose a method to highlight query hits in hierarchically clustered collections of interrelated items such as digital libraries or knowledge bases. The method is based on the idea that organizing search results similarly to their arrangement on a fixed reference map facilitates orientation and assessment by preserving a user's mental map. Here, the reference map is built from an MDS layout of the items in a Voronoi treemap representing their hierarchical clustering, and we use techniques from dynamic graph layout to align query results with the map. The approach is illustrated on an archive of newspaper articles.

Confirmatory factor analysis of the Dutch Intolerance of Uncertainty Scale: Comparison of the full and short version

Background and objectivesThe Intolerance of Uncertainty Scale (IUS) was developed for assessing reactions to ambiguous situations, uncertainty, and future events. The IUS has been validated in different languages, but equivocal factor structures, in combination with highly interrelated items and factors, resulted in a redundancy of the items of the English version. In the current study, the psychometric properties of the Dutch version of the IUS were examined, and compared with the shortened 12-item version (IUS-12). MethodsConfirmatory factor analyses were used to investigate different factor structures of both the full and short version of the IUS. ResultsResults indicated that the IUS-12 model with two factors (Prospective Anxiety and Inhibitory Anxiety) provides the best fit. The reduced measure has equally good internal consistency, and is highly correlated with the full version. LimitationsFuture research could investigate whether the current findings generalize to clinical populations. ConclusionTo summarize, the usage of the short 12-item version of the IUS should be encouraged in future research concerning intolerance of uncertainty.

Passion by Design: Meditations on an Innovative Model

In this article the author discusses two intrinsically interrelated items: first, the past, present, and future of the educational infrastructure as well as its purpose, potential, and shortcomings; and second, the unique nature of his home school education. He further examines related elements that are conducive to the enrichment of the current educational model and also that of social development in general. His underlying goal is to inspire reflection on the importance of fully promoting passion, diversity, creativity, introspection, and innovation.

Psychophysiology of False Memories in a Deese-Roediger-McDermott Paradigm with Visual Scenes

Remembering something that has not in fact been experienced is commonly referred to as false memory. The Deese-Roediger-McDermott (DRM) paradigm is a well-elaborated approach to this phenomenon. This study attempts to investigate the peripheral physiology of false memories induced in a visual DRM paradigm. The main research question is whether false recognition is different from true recognition in terms of accompanying physiological responses.Sixty subjects participated in the experiment, which included a study phase with visual scenes each showing a group of interrelated items in social contexts. Subjects were divided into an experimental group undergoing a classical DRM design and a control group without DRM manipulation. The control group was implemented in order to statistically control for possible biases produced by memorability differences between stimulus types. After a short retention interval, a pictorial recognition phase was conducted in the manner of a Concealed Information Test. Simultaneous recordings of electrodermal activity, respiration line length, phasic heart rate, and finger pulse waveform length were used. Results yielded a significant Group by Item Type interaction, showing that true recognition is accompanied by greater electrodermal activity than false recognition.Results are discussed in the light of Sokolov's Orienting Reflex, the Preliminary Process Theory and the Concealed Information Test. Implications and restrictions of the introduced design features are critically discussed. This study demonstrates the applicability of measures of peripheral physiology to the field of false memory research.

Graphical probabilistic analysis of the combination of items of evidence

Unlike the evaluation of single items of scientific evidence, the formal study and analysis of the joint evaluation of several distinct items of forensic evidence has to date received some punctual, rather than systematic, attention. Questions about the (i) relationships among a set of (usually unobservable) propositions and a set of (observable) items of scientific evidence, (ii) the joint probative value of a collection of distinct items of evidence as well as (iii) the contribution of each individual item within a given group of pieces of evidence still represent fundamental areas of research. To some degree, this is remarkable since both, forensic science theory and practice, yet many daily inference tasks, require the consideration of multiple items if not masses of evidence. A recurrent and particular complication that arises in such settings is that the application of probability theory, i.e. the reference method for reasoning under uncertainty, becomes increasingly demanding. The present paper takes this as a starting point and discusses graphical probability models, i.e. Bayesian networks, as framework within which the joint evaluation of scientific evidence can be approached in some viable way. Based on a review of existing main contributions in this area, the article here aims at presenting instances of real case studies from the author’s institution in order to point out the usefulness and capacities of Bayesian networks for the probabilistic assessment of the probative value of multiple and interrelated items of evidence. A main emphasis is placed on underlying general patterns of inference, their representation as well as their graphical probabilistic analysis. Attention is also drawn to inferential interactions, such as redundancy, synergy and directional change. These distinguish the joint evaluation of evidence from assessments of isolated items of evidence. Together, these topics present aspects of interest to both, domain experts and recipients of expert information, because they have bearing on how multiple items of evidence are meaningfully and appropriately set into context.

Modeling Complex Multi-Issue Negotiation Using Utility Graphs

Automated bilateral negotiation forms an important type of interaction in agent based systems for electronic commerce; it allows seller and customer to determine the terms and content of the trade iteratively and bilaterally. Consequently, deals may be highly customized (especially for complex goods or services) and highly adaptable to changing circumstances. Moreover, by automating the negotiation process, the potentially time-consuming process is delegated to autonomous software agents who conduct the actual negotiation on behalf of their owners. In this paper, we consider the problem of a seller agent negotiating bilaterally with a customer about selecting a subset from a collection of goods or services, viz. the bundle, together with a price for that bundle. The techniques developed in this paper try to benefit from the so-called win-win opportunities, by finding mutually beneficial alternative bundles during negotiations. To facilitate the search for win-win opportunities the developed techniques rely on a continuously updated model of the opponent's preferences. Several papers on multiagent negotiation have already focused on finding win-win opportunities through opponent modeling. However, these papers only consider preference relations for which the issues have independent valuations. In this paper we study the considerably harder problem of interdependencies between issues. In order to model such complex interdependencies between items, we introduce the novel concept of utility graphs. Utility graphs build on the idea that highly nonlinear utility functions, which are not decomposable in sub-utilities of individual items (such as in the seminal work of Raiffa), may be decomposable in sub-utilities of clusters of inter-related items. They mirror, to a certain extent, the graphical models developed in (Bayesian) inference theory. The idea, behind using utility graphs in a one multi-issue bargaining setting, is to provide the seller with a formalism for exploring the exponentially large bundle space, efficiently. In this paper, we show how utility graphs can be used to model an opponent's (i.e. customer's) preferences. Moreover, we also propose an updating procedure to obtain approximations of the customer s utility graph indirectly, by only observing his counter-offers during the negotiation. At the start of a negotiation process, the seller's approximation of the customer's utility graph represents some prior information about the maximal structure of the utility space to be explored. This prior information could be obtained through a history of past negotiations or the input of domain experts. (An important advantage of utility graphs is that they can handle both qualitative and quantitative prior information.) After every (counter) offer of the customer, this approximation is refined. Conducted computer experiments show that by using only a fairly weak assumption on the maximal structure of customers utility functions the updating procedure enables the seller to suggest offers that closely approximate Pareto efficiency. By using utility graphs, Pareto-efficiency can be reached with few negotiation steps, because we explicitly model the underlying graphical structure of complex utility functions of the Buyer and use it to explore the outcome space. Consequently, our approach is applicable to time constrained negotiations, or negotiations where the impatience of one of the parties is a limiting factor. Furthermore, unlike other solutions for high-dimensional negotiations, the proposed approach does not require a mediator.

Lexical base as a compressed language model of the world (on material from the Ukrainian language)

Lexical base as a compressed language model of the world (on material from the Ukrainian language) In the article the fact is verified that the list of words selected by formal statistical methods (frequency and functional genre unrestrictedness) is not a conglomerate of non-related words. It creates a system of interrelated items and it can be named the "lexical base of language". This selected list of words covers all the spheres of human activities. To verify this statement the invariant synoptical scheme common for ideographic dictionaries of different languages was determined.

Issues in Computational Vickrey Auctions

:The Vickrey auction has been widely advocated for multiagent systems. This protocol has several limitations (lower revenue than with alternative protocols, lying in non-private-value auctions, bidder collusion, lying auctioneers, undesirable revelations of sensitive information), and they are reviewed so as to guide practitioners in deciding when to use it. The special characteristics of Internet auctions are also discussed (third-party auction servers, cryptography, how proxy agents relate to the revelation principle and fail to promote truth telling), as well as several limitations of the protocol that stem from considerations of computational complexity (inefficient allocation and lying in sequential auctions of interrelated items, untruthful bidding under valuation uncertainty, counterspeculation to make deliberation-control or information-gathering decisions). Finally, methods for determining winners and prices in combinatorial"second-price” auctions are treated, together with the implications for truth dominance.