Relative Degree Of Importance Research Articles

Evaluating the spatiotemporal dynamics of driving factors for multiple drought types in different climate regions of China

Extreme weather events and natural hazards are increasingly prevalent with global warming, making it imperative to focus on the driving factors and dynamic changes associated with drought—a disaster with widespread impacts and significant losses. This study used a longtime-series of ERA5 reanalysis data to elucidate the dynamic change characteristics of direct and indirect driving factors across China. Ground observation data spanning an extended period were used to estimate the performance of the reanalysis data. The selected driving factors for drought included precipitation (PRE), potential evapotranspiration, soil moisture (SM), bare soil evapotranspiration (BSET), vegetation canopy evapotranspiration (VCET), subsurface runoff (SUBRO), surface runoff (SURRO), temperature (TEM), specific humidity, and near-surface wind speed (WS). Meteorological drought, hydrological drought, and agricultural drought were delineated using the Standardized Precipitation Evapotranspiration Index, Standardized Runoff Index, and Standardized Soil Moisture Index. The Random Forest method was used to ascertain the relative importance of driving factors and their main contributions to different types of droughts. The findings revealed that ERA5 exhibited great simulation performance compared to observational data. However, the correlation coefficient of SM might be relatively lower than that of other driving factors across China. Notably, BSET and PRE exhibited higher relative importance degrees (RID) for meteorological drought in subregions 1, 2, and 3. At the same time, SUBRO demonstrated a more statistically significant influence in subregions 3, 4, and 5. SURRO played a statistically significant role in subregion 1. Moreover, BSET and PRE were pivotal for meteorological drought in northern China, whereas VCET assumes importance for hydrological drought in southern China and the North China Plain. Across different types of droughts, PRE, SM, and TEM generally exhibited relatively large RID values in China. The dynamic changes in RID for both direct and indirect driving factors varied for different drought types across different subregions of China. Notably, the RID displayed contrasting dynamic changes between PRE and BSET for meteorological drought in most subregions. Additionally, the degree of relative importance of near-surface WS tended to be lower for hydrological drought across various subregions. This study contributed to a deeper understanding of drought evolution and enhanced monitoring efforts.

Employing the strategy of cognitive trips in physics

The study aimed to employ the strategy of knowledge journeys with the flavor of the Palestinian novel in physics, and to present proposals and recommendations based on the results of this study. The problem of the study was represented by the question: What is the degree of relative importance of employing the strategy of cognitive journeys with the flavor of the Palestinian novel in physics? What are the possible suggestions for that? Use the descriptive analytical method. The study population consisted of physics teachers and students for the tenth and eleventh scientific grades. Whereas the sample was random and stratified, as it numbered (21) male and female physics teachers from Bethlehem governmental schools. The study tool is a questionnaire consisting of three axes and the design of knowledge trips through (Web Quest). One of the most important findings of the researcher was that there are statistically significant differences at the level of significance (α ≤ 0.05) between the averages of the degree of relative importance of employing the cognitive journeys strategy with the flavor of the Palestinian novel in physics, due to the gender variable in favor of males. There are statistically significant differences at the level of significance (α ≤ 0.05) between the averages of the relative importance of employing the strategy of cognitive journeys with the flavor of the Palestinian novel in physics due to the grade variable in favor of the scientific eleventh. The most important recommendation is that the Palestinian Curriculum Committee should reconsider publishing the Palestinian novel. Developing students' positive attitudes towards physics. Enhancing many skills such as research, exploration and criticism, in addition to linking scientific and national knowledge through induction competitions in this field. Keywords: Cognitive Journeys - Cognitive Journeys Strategy - Palestinian Novel – Physics.

4차산업혁명시대하의 한국 물류산업의혁신정책 우선순위에 관한 연구

The purpose of this study is to analyze priorities of the recent Korean distributional policies to contribute establishment of the efficient midꠓand long term distributional policies. For this purpose, the important factors on the distributional policies were selected through the literature review, and conducted questionnaire to the experts on distributional industry using these factors. The factors selected for the first level AHP analysis were Promotion and Support of Distributional Industry, Establishment and Maintenance of Distributional Infra, Development and Proliferation of High frontier Distributional Technology, and Creation of Distributional Industry Environment. And factors selected for the second level AHP analysis were 15 factors. The results are as follows: The first level weighted value and relative importance degree showed that Promotion and Support of Distributional Industry was the highest on priority, Establishment and Maintenance of Distributional Infra was the second, Development and Proliferation of High frontier Distributional Technology was the third, and Creation of Distributional Industry Environment was the fourth. And most of lower level factors meet high position of 50 percent are Promotion and Support of Distribution Industry and Establishment and Maintenance of Distributional Infra. Through the results of this study, to establish the long time sustainable distribution policy for the growth and improvement of efficacy of domestic distribution industry, it needs to present intensive counter-move strategies on the each areas shown the higher priorities.

A Constrained Control Allocation and Tuning Scheme for Hybrid Actuators in Spacecraft Attitude Control

Agile rotational maneuvers of spacecraft requires careful execution since its actuators may not be able to produce the demanded torques, causing the state trajectories to deviate and a desired attitude would not be guaranteed. We investigate the control allocation problem for a redundant set of hybrid actuators that include reaction wheels, magnetorquers, and continuous-force thrusters. The main objective of the magnetorquers is to dump momentum from the reaction wheels, whereas the wheels are the primary actuators in attitude control, and more agile maneuvers or faster unloading of momentum can be handled by the thrusters. A modified mixed optimization scheme for control allocation is presented where the equality constraints account for satisfying the high-level (virtual) control inputs for both attitude control and momentum dumping. Variants of dynamic weights in the optimization are developed such that magnetorquers and thrusters may contribute with a relative degree of importance in the attitude control problem. The control allocation scheme is solved using quadratic programming where simulation results are shown for fast and slow rotational maneuvers together with fast and slow momentum dumping.

Relationship Between Extreme Rainfall Occurrences and Galactic Cosmic Rays over Natal/RN, Brazil: A Case Study.

The increase in Galactic Cosmic Rays (GCR) flux intensity induces the Condensation Nuclei (CN) production, which intensifies rainfall occurrences. Then, the objective of this study was to analyze the rainfall distribution in the NEB and the impact of GCR flux on extreme rainfall events occurred in July 1998 in Natal/RN, Brazil. We used historical rainfall, Sea Surface Temperature (SST) and GCR flux data for Natal/RN. We used R software for statistical analysis. The results indicate that the GCR flux is important for intensifying extremes rainfall occurrences. This fact is observed when analyzing the relationship between rainfall greater than 10 mm and GCR flux above 6,390 counts/min. Pearson correlation coefficient between rainfall and GCR flux was 0.94 (p-value = 0.0005) and SST was -0.76 (p-value = 0.0263), both statistically significant. The rate between GCR flux and rainfall was +2.87 mm/count/min, while the rate between SST and rainfall was -7.91 mm/°C. The variance proportion explained by regression was 94.41%, with relative importance degree corresponding to 62.0% for GCR flux and 32.4% for SST, respectively. The results show that GCR flux had a greater contribution to extreme rainfall occurrence in the metropolitan region of Natal/RN and it is important in climatological studies.

The Extent to which Jordanian Commercial Banks Accomplish Internal Accounting Control Objectives

This study aimed to identify the extent to which Jordanian commercial banks achieve the objectives of internal accounting control, and the study relied on the descriptive and inferential approach. The exclusion of (4) questionnaires due to the lack of completeness of their answers. Consequently, the number of valid questionnaires for the purposes of analysis reached (164) employees of the internal control departments in Jordanian commercial banks.
 The results showed that the attitudes of the accounting internal control staff towards the dependent variable, the accounting internal control objectives in the Jordanian commercial banks, have achieved high degrees of relative importance. Also, the dimension (effectiveness and efficiency of operations) achieved the first rank with a very high degree of approval among the dimensions of the internal accounting control objectives. The second rank achieved the dimension (compliance with laws and legislation) with a high degree of approval. The dimension (reliability and completeness of financial reports) achieved the last rank with a high degree of approval.
 The study concluded with a set of recommendations, the most important of which are: improving the effectiveness and efficiency of operations by implementing appropriate control procedures in the bank to increase the efficiency of resource utilization in the bank, and measuring the effectiveness and efficiency of the applied control means through the presence of an internal audit system in accordance with the approved audit procedures.

Finding the optimal CO2 adsorption material: Prediction of multi-properties of metal-organic frameworks (MOFs) based on DeepFM

Metal-organic frameworks (MOFs) has been widely considered as promising candidates for CO2 adsorption due to their high porosity and high structural adjustability. By combining the various properties of MOFs, an MOFs material-property matrix can be obtained to find the best MOFs for different applications, but this matrix is often incomplete in practice. In this work, a DeepFM model was developed to predict the multi properties of CO2 absorbents with limited start-up training data, but with high prediction accuracy. The DeepFM model contains deep neural network, with better non-linear fitting ability, thus significantly improved the prediction accuracy. Meanwhile, by adding the descriptors of MOFs as the input data of new features, the DM model also alleviates the cold start problem. By predicting 28 adsorption properties of 8206 screened hypothetical MOFs, 7 high-performance MOFs for CO2 adsorption were selected. In addition, the model also helps to find out the relative importance degree of various descriptors on the CO2 capture capabilities of MOFs, which is of great help in future MOFs synthesis.

Exploration of Regional Public Digital Culture Service Mode Based on Artificial Intelligence Technology

In order to improve the development of China’s public cultural service supply field, this paper introduces the public culture of weather services with the support of network intelligence services and smart devices. This article examines the purpose of public cultural cloud services to identify current public service public culture and future development models; The results show that the average consistency indexes determined by the weight of evaluation indexes based on AHP are 0, 0, 0.514, 0.894, 1.118, 1.248, 1.344, 1.421, and 1.461, respectively. Their importance order is judged according to the degree of relative importance. According to the hierarchy and diversity of users’ needs, a “multiple” development model of public digital culture cloud service is constructed. This data provides a measure of resistance to innovations in public cloud culture, data advocacy to support public climate change in four areas: the reform of service strategies, the integration of public culture, the development of specialized resources, and the development of service teams and customer capabilities.

TRISO SiC Failure Probability for Reactivity Initiated Accidents in High-Temperature Gas-Cooled Reactors

This work analyzes the failure process of the silicon carbide (SiC) layer in tristructural isotropic (TRISO) during reactivity-initiated accident scenarios for a high-temperature gas-cooled reactor (HTGR) with BISON. Two cases are considered—a group control rod withdrawal (CRW) and a control rod ejection (CRE)—reproduced from a previous study. Failure probability is modeled using Weibull statistics, and worst-case scenario Weibull parameters are adopted to simulate the envelopes in BISON with a one-dimensional TRISO model. CRW scenario results are characterized by higher values of maximum energy deposition and final temperature and volumetric strain with respect to the CRE ones, but the latter have remarkably higher SiC failure probability, mainly due to the offset in strain rates between the two cases. This work also confirms the validity and conservatism of the performance envelopes produced in a previous work by replicating the envelope formulation using RELAP5-3D and RAVEN with a different sampling technique and obtaining consistent results. A sensitivity analysis using the Sobol variance decomposition method on SiC failure probability is then performed involving a set of inputs on both CRW and CRE. The two most important parameters are Weibull modulus and characteristic stress, and their relative importance depends on the specific case. The proposed interpretation of the results is that both energy deposition and strain rate influence the relative degree of importance of the failure parameters. Computation of 95% confidence intervals around worst-case scenario SiC failure probability values is also carried out for four different sets of Weibull parameters. A new criterion for SiC TRISO quality classification built upon safety-based ranges of Weibull parameters is proposed to be integrated in future Fuel-Production Quality Assurance Plans.

Risk Impact of Maintenance and Other Activities with Regard to Plant Areas

In all operating modes of a nuclear power plant a lot of activities take place, including maintenance, surveillance testing and plant modifications. Some of these activities can impose temporary increase in risk level, as they may change the status of equipment important to plant safety. Such risk increases are usually controlled by risk monitoring, which considers changes in risk due to changes in the status (e.g. availability) of plant systems and functions. Risk monitors are, in many cases, designed and operated to be system-oriented (or function-oriented), as they focus on “measuring” the risk associated with different system configurations (from where comes the often used term “configuration risk management”). On the other hand, components of plant systems are placed in various locations and at various floors (elevations) of plant buildings. Piping, as well as cabling, is routed through one or more buildings. Equipment performing different functions is, sometimes, located near each other due to architectural limitations. Where required, barriers are applied in order to ensure physical separation and independency. Due to these reasons, a particular plant area (compartment, room, part of a large room,…) can contain a variety of mechanical, electrical and / or other equipment with different safety implications. As well as system components, plant areas are also related to each other, with different degrees of relative importance. Since activities performed in different plant areas can imply changes, actual or potential, in the status of associated equipment, structures and / or barriers, there is also a need that risk monitoring considers the area-oriented aspects, beside considering those which are system-oriented or function-oriented. Risk impact of an activity taking place in a particular plant area can be considered in terms of changes (potential or actual) to its three components: 1) likelihood of initiators which can be triggered by equipment in the area (but which are not mitigated by any of the equipment in the same area); 2) mitigating capability regarding the initiators which are not triggered in this area; 3) likelihood of initiators triggered in this area and mitigating capability regarding the same initiators. Activity in a particular plant area may be related to none or to any combination of the three risk impact components. Normally, risk impact under 3) is limited by the architectural engineering principles (because it may become very large risk contributor). However, it may be present in some residual form and it cannot be excluded (as demonstrated by area-related risk studies performed in the past, such as internal fire and internal flooding analyses). With activity taking place in a particular area, the relevant importance of any other plant area (and, hence, potential risk impact of any activity that may be planned to go on at the same time) is then considered in terms of, respectively: 1) whether it contains the equipment relevant for mitigation of initiators that can be triggered in the considered area; 2) whether it includes the potential for triggering an initiator which is mitigated by the equipment located in the considered area; 3) whether it contains the relevant mitigation equipment or include the potential for relevant initiators. The paper discusses these and other related issues and describes some basic concepts for the area-oriented risk management.

Label enhancement-based feature selection via fuzzy neighborhood discrimination index

As an essential preprocessing step, the feature selection for multi-label classification is an efficient tool to solve the high-dimensionality in training data under multiple semantics. However, in multi-label learning, the assumption of a uniform distribution is not practicable for some real applications where labels are related to the instance with different relative importance degrees. Motivated by this, the label enhancement algorithm is utilized to transform the logical values in multiple labels into label distributions with real values. To maintain the supervised information of label distributions, the discrimination index is combined with granular theory to describe the vagueness in feature and label spaces, and the fuzzy neighborhood discrimination index is presented to assess the discrimination information in features with continuous values. Furthermore, an innovative label enhancement-based feature selection approach is designed to handle the challenge caused by high dimensionality with multiple semantics. In this algorithm, the fuzzy conditional discrimination index is proposed by combining the fuzzy neighborhood discrimination index under the feature subset with the label similarity matrix of label distributions, and the significance of the feature is measured by the fuzzy conditional discrimination index, which is utilized to assess the discernibility information of labels under the feature. Furthermore, to validate the efficacy of our algorithm, a collection of experiments are conducted with five representative multi-label feature selection methods under twelve benchmark datasets. With six widespread evaluation indicators of multi-label classification, the experimental results indicate that our algorithm obtains superior performance against other algorithms.

Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection

ELECTRE is a family of multi-criteria decision analysis techniques, which has the ability to provide as much as possible precise and suitable set of actions or alternatives to the underlying problem by eliminating the alternatives, which are outranked by others. Group decision-making is an effective process to provide the most appropriate solution to real-world decision-making scenarios by considering and merging the expert opinions of multiple individuals on the problem. The aim of this study is to present an extended version of the ELECTRE I model called the Fermatean fuzzy ELECTRE I method for of multi-criteria group decision-making with Fermatean fuzzy human assessments. The method proposed in this study has the possibility to solve multi-criteria group decision-making problems by using the Fermatean fuzzy decision matrix obtained in Fermatean fuzzy number form in the evaluations made with the available alternatives based on expert opinions. First, the mathematical description of the multi-criteria group decision-making problem with Fermatean fuzzy information has been given. Then, the proposed Fermatean fuzzy ELECTRE I method to deal with the problem has been presented. After the determination of the relative importance degree of experts, the Fermatean fuzzy aggregated averaging operator is employed to merge the individual Fermatean fuzzy decision matrices produced by the experts into the aggregated Fermatean fuzzy decision matrix. Next, for pairwise comparison of available alternatives with respect to considered criteria, the concepts of Fermatean fuzzy strong, midrange, and weak concordance and discordance sets are based on the approach of score function and accuracy function defined for Fermatean fuzzy numbers. Afterward, Fermatean fuzzy concordance and discordance matrices are defined, constructed by concordance and discordance indices. Finally, Fermatean fuzzy effective concordance and discordance matrices are computed to obtain Fermatean fuzzy aggregated outranking matrix, indicating abstract information on dominations of suitable alternatives to the others. The proposed method will be used in material selection in distinct implementations, exclusively in biomedical applications where the prosthesis materials should have similar characteristics to human tissues. Since biomedical materials are used in various parts of the human body for many different purposes, in this study, material selection will be made using the method presented for the femoral component of the hip joint prosthesis for orthopedists and practitioners who will choose biomaterials.

An interval 2-Tuple linguistic Fine-Kinney model for risk analysis based on extended ORESTE method with cumulative prospect theory

The risk assessment is one of the most significant procedures for identifying, preventing, and controlling Occupational Health and Safety (OHS) risks. One of many kinds of techniques for OHS risk assessment is based on the Fine-Kinney model. Most of the Fine-Kinney-based risk assessment approaches can consider the relative importance degree of risk parameters. Nevertheless, the current Fine-Kinney-based risk assessment approaches do not have abilities to capture the reference dependence effects and detailed relationships among hazards. In addition, these approaches overlook the influence of the deviation of risk evaluation information. To overcome these limitations, in this paper, an improved Fine-Kinney model is proposed for OHS risk assessment by integrating the weighted power average (WPA) operator, ORESTE (Organísation, rangement et Synthèse de données relarionnelles (in French)) method, and cumulative prospect theory. First, the interval 2-Tuple linguistic variables are adopted to transform linguistic risk information into quantitative risk rating information. Then, an extended WPA operator is proposed to fuse the risk evaluation information from decision-makers, in which an optimization model is constructed to determine the weights of decision-makers. Next, an extended ORESTE method based on cumulative prospect theory and interval 2-Tuple linguistic variables is incorporated into the Fine-Kinney model to prioritize OHS risk. After that, the OHS risk assessment of the automobile components manufacturing process is presented to test the applicability and rationality of the improved Fine-Kinney model. After that, a sensitivity analysis is conducted to further illustrate the proposed model. Finally, the comparative analyses between the proposed risk assessment approach and other Fine-Kinney models are led to illustrating its effectiveness and advantages.

A NOVEL DECISION-MAKING FRAMEWORK BASED ON PROBABILISTIC LINGUISTIC TERM SET FOR SELECTING SUSTAINABLE SUPPLIER CONSIDERING SOCIAL CREDIT

Sustainable supplier selection (SSS) has become an essential task for decision-makers in competitive environments. We construct a new decision-making framework for SSS. First, classical SSS usually includes fixed factors in environmental, social and economic dimensions. Differently, we adopt new social factors from credit perspective with corporate social credit system being promoted vigorously by the Chinese government. Next, we employ probabilistic linguistic term sets (PLTSs) to collect experts’ judgments about interactive influence between factors. Third, we combine PLTSs with Decision Making Trial and Evaluation Laboratory (DEMATEL) method to identify critical success factors (CSFs) for improving decision-making efficiency. And we also give definition to relative importance degree, standard relative importance degree, deviation of importance degree and influence degree to reflect the interactive influence between factors. To eliminate subjective influence, we combine entropy weighting approach and DEMATEL to compute weights. Fourthly, we redefine dominance degree and apply it into TODIM method for SSS. Finally, the proposed decision-making framework’s effectiveness is verified by using the case study of a new energy vehicle (NEV) company. Based on this, sensitivity analysis and comparison of methods are conducted. The results verify that the decision-making framework is valid and effective for SSS.

Language attitudes and identity building in the linguistic landscape of Montreal

Abstract Few studies to date have considered the agency of readers in reinterpreting the cultural, historical, political, and social background of the linguistic landscape (LL; visible language in public space) and the ways in which individual and collective identities are discursively conceptualised through the LL. In this article, we present results from a study involving participants from three self-described sociolinguistic identities (Francophone, Anglophone, and Bilingual), reading signs found in the LL of Montreal. Using photographic prompts, we questioned participants about the probable location of signs, their languages, and the languages’ placement on monolingual (French or English) and bilingual (French–English) signs emanating from both governmental and private entities. Further discussions about their emotive responses to the signs presented and the possible responses of “others” reveal the relative degrees of importance attached to these linguistic elements in constructing, negotiating, and communicating various and (more) fluid sociolinguistic identities.

An integrated identification approach of agile engineering characteristics considering sensitive customer requirements

Under the effects of internal factors and the external environment, some customer requirements can be extremely sensitive to change; these are known as sensitive requirements. These requirements drive the identification and design of the relevant engineering characteristics. This study defines such characteristics as agile engineering characteristics (AECs), which are crucial for redesigning products and prolonging their service life but attract limited research and practice attention. The research question of identifying the product AECs is answered in this study, and three key issues are solved in the proposed methodology. First, the contribution of product engineering characteristics to customer requirements is calculated using an orthogonal experimental design, and the optimal level combination of engineering characteristics to satisfy the customer requirements is determined through the analysis of variation. Subsequently, the relative importance of engineering characteristics is calculated based on data envelopment analysis. Next, an agility index is defined by integrating the customer satisfaction degree, the relative importance degree of engineering characteristics, and the variation of the level of engineering characteristics, to identify the AECs. Finally, based on the identification of the AECs of an electric vehicle air-conditioning heat pump scroll compressor, the results are analysed and compared to illustrate the feasibility and effectiveness of the proposed method.

РЕЛЬЕФООБРАЗУЮЩИЕ ФАКТОРЫ ТРАНСФОРМНЫХ РАЗЛОМОВ АТЛАНТИЧЕСКОГО ОКЕАНА

Transform faults are widespread within the Atlantic Ocean. Their relief is determined by a variety of factors related mainly to the peculiarities of the deep structure of the lithosphere and regional geodynamics. The degree of their influence changes when passing from one morphotectonic province of the Atlantic Ocean to another. The differences are manifested in the morphology of the main elements of the transform fault and the correlation of their morphometric parameters with the length of the active part, which was shown earlier by analogue modeling. The dependence between the depth of the transform valley and the axis offset of the Mid-Atlantic Ridge along the transform fault has been revealed. Variations in the values of morphometric parameters are interpreted as a consequence of different duration of fault development, as well as different degrees of influence of secondary factors within each of the provinces. Based on the analysis of the bathymetric data on the Atlantic transform faults, five main groups of relief-forming factors are identified, and the relative degree of importance of the factors is determined for each of them. It is assumed that the identified dependences are preserved for the transform faults in other oceans.

Tax Revenue Forecast Errors: Wrong Predictions of the Tax Base or the Elasticity?

In this paper, we disentangle tax revenue forecast errors into influences stemming from wrong macroeconomic assumptions and false predictions of the elasticities linking the tax base to its corresponding tax type. Across six tax types and the overall tax sum for Germany, we find a heterogeneous degree of relative importance of both sources. Whereas wrong macroeconomic assumptions matter most for profit-related taxes and the wage tax, false predictions of the elasticities mainly drive the forecast errors of the energy tax and the sales taxes. For the overall tax sum, more than two-third of the error can be attributed to wrong macroeconomic predictions and approximately one-third to false assumptions on the elasticity. Our results suggest that outsourcing the macroeconomic projections to an independent forecaster and methodological improvements can reduce tax revenue forecast errors.

Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach.

In context to high-dimensional clustering, the concept of feature weighting has gained considerable importance over the years to capture the relative degrees of importance of different features in revealing the cluster structure of the dataset. However, the popular techniques in this area either fail to perform feature selection or do not preserve the simplicity of Lloyd's heuristic to solve the k-means problem and the like. In this paper, we propose a Lasso Weighted k-means ( LW- k-means) algorithm, as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ( p) can be much higher than the number of observations ( n). The LW- k-means method imposes an l1 regularization term involving the feature weights directly to induce feature selection in a sparse clustering framework. We develop a simple block-coordinate descent type algorithm with time-complexity resembling that of Lloyd's method, to optimize the proposed objective. In addition, we establish the strong consistency of the LW- k-means procedure. Such an analysis of the large sample properties is not available for the conventional sparse k-means algorithms, in general. LW- k-means is tested on a number of synthetic and real-life datasets and through a detailed experimental analysis, we find that the performance of the method is highly competitive against the baselines as well as the state-of-the-art procedures for center-based high-dimensional clustering, not only in terms of clustering accuracy but also with respect to computational time.

User segmentation via interpretable user representation and relative similarity-based segmentation method

User segmentation is an essential element of marketing and product development that considers customers’ needs and recognizes the heterogeneity of those needs. In a key study of smartphone user segmentation, Lee et al. analyzed app usage sequencing using seq2seq architecture. However, despite achieving meaningful results, their approach could not provide a robust interpretation of user segmentation because the seq2seq architecture represented users in a continuous vector space generated from a black box model. In this paper, we propose an interpretable user representation method that combines app clustering with a novel segmentation method. The user representation clusters characteristically similar apps into common clusters, with each user represented by their frequencies of app use within their respective clusters. Two novel techniques are also applied to normalize the value of user representation based on the relative degrees of importance between app clusters and the membership strengths of individual apps within a cluster. Furthermore, to address the limitations of existing segmentation methods, in which the most closely located users are assigned to specific clusters, the proposed method segments represented users using a novel segmentation approach based on relative similarity. Experimental results demonstrate that the proposed method provides an intuitive interpretation for each user’s representation and segmentation results. Furthermore, we effectively show the similarities between the results produced by our method and ground truth and demonstrate that it outperforms existing user segmentation methods.