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Article
Publication date: 3 June 2021

Lulu Ge, Zheming Yang and Wen Ji

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability…

Abstract

Purpose

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

Design/methodology/approach

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Findings

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

Practical implications

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

Originality/value

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

Details

International Journal of Crowd Science, vol. 5 no. 2
Type: Research Article
ISSN: 2398-7294

Keywords

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Book part
Publication date: 26 October 2017

Ronald K. Klimberg, Samuel Ratick and Harvey Smith

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series…

Abstract

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series dataset contains numerous observations of important predictor variables that can effectively be classified into groups based on their values. In such situations, cluster analysis is often employed to improve the MLR models predictive accuracy, usually by creating separate regressions for each cluster. We introduce a novel approach in which we use the clusters and cluster centroids as input data for the predictor variables to improve the predictive accuracy of the MLR model. We illustrate and test this approach with a real dataset on fleet maintenance.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78743-069-3

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Book part
Publication date: 21 November 2018

Nur Syazwin Mansor, Norhaiza Ahmad and Arien Heryansyah

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the…

Abstract

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Details

Improving Flood Management, Prediction and Monitoring
Type: Book
ISBN: 978-1-78756-552-4

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Book part
Publication date: 9 June 2020

Anna Purwaningsih and Indra Wijaya Kusuma

This study examines associations between accrual earnings management (AEM) and real earnings management (REM), and earnings quality between countries considered under…

Abstract

This study examines associations between accrual earnings management (AEM) and real earnings management (REM), and earnings quality between countries considered under insider economics and outsider economics clusters. Countries included in the outsider economics cluster are Singapore, Malaysia, and Hong Kong. Meanwhile, countries included in the insider economics cluster are Indonesia, the Philippines, and South Korea. Earnings management practices have changed from AEM to REM since the publication of the Sarbanes Oxley Act and DFA 954 implementation of the Claws back provision policy in the United States.

Research data were obtained from the Bloomberg database, 2010–2016. Regression analysis and t-test were utilized. This study compared AEM and REM to determine which is stronger based on country clusters, as well as the association between AEM or REM and earnings quality.

The results of this study indicate that AEM and REM are associated with the quality of earnings in the insider economics cluster. However, AEM and REM are not associated with earnings quality in the outsider economics cluster. Furthermore, associations between AEM and earnings quality are stronger than associations between REM and earnings quality in insider economics cluster.

Abstract

Details

Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry
Type: Book
ISBN: 978-1-78743-086-0

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Article
Publication date: 13 September 2021

Viviana Elizabeth Zárate-Mirón and Rosina Moreno Serrano

This paper aims to evaluate whether the integration of smart specialization strategies (S3) into clusters significantly impacts their efficiency for countries that still…

Abstract

Purpose

This paper aims to evaluate whether the integration of smart specialization strategies (S3) into clusters significantly impacts their efficiency for countries that still do not implement this policy. This study tests three effects: whether the kind of policies envisaged through an S3 strategy impacts cluster’s efficiency; whether this impact changes with the technological intensity of the clusters; to determine which S3 is more suitable for sub-clusters at different levels of technological intensity.

Design/methodology/approach

The Mexican economy is taken as case of study because it has a proper classification of its industries intro Porter’s cluster’s definition but still does not adopt the S3 policy. Through data envelopment analysis (DEA), this study evaluates the cluster’s efficiency increment when variables representing the S3 elements are included.

Findings

The results show that strategies following the S3 had a significant impact in all clusters, but when clusters were classified by technological intensity, the impact on efficiency is higher in clusters in the medium low-tech group.

Practical implications

According to the results in the DEA, it can be concluded that these S3 strategies have the potential to increase the clusters’ productivity significantly. These results make convenient the adoption of the S3 policy by countries that already count with a properly cluster definition.

Originality/value

These findings contribute to the lack of studies that analyze the join implementation of S3 on clusters.

Details

Competitiveness Review: An International Business Journal , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1059-5422

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Article
Publication date: 9 September 2021

Deepak Chawla and Himanshu Joshi

India has the second highest percentage of mobile wallet adoption driven by availability of affordable smartphones and Internet. Despite a general interest, studies on its…

Abstract

Purpose

India has the second highest percentage of mobile wallet adoption driven by availability of affordable smartphones and Internet. Despite a general interest, studies on its adoption have been scarce. This research assumes that user segments exist, each with their own level of maturity, and addresses the question “Are there segments which can be profiled?” Thus, the objectives of the study are to propose a model that explains the attitude of user segments towards its adoption; identify probable user segments and profile them; examine the importance and performance of constructs which influence attitude within each cluster and recommend ways to improve performance.

Design/methodology/approach

This paper employs the constructs from two popular theories on technology adoption, i.e. technology acceptance model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT). A synthesis of review of literature on these models, besides two focus group discussions (FGDs), was used to design a pilot instrument. A nationwide survey was conducted, and 744 responses were obtained. Convenience sampling was used to select the respondents. The average scores of various constructs were computed and subjected to hierarchical clustering. Further, k-means clustering was carried out. The demographic profiling of each cluster was done through cross-tabulation and differences related to attitude and intention between clusters were tracked by one-way Analysis of Variance (ANOVA). To determine the relative importance and performance of constructs within each cluster, Importance-Performance Map Analysis (IPMA) using Smart Partial Least Squares (PLS) was carried out.

Findings

The hierarchical clustering resulted in three clusters. The result of k-means clustering was used to label the clusters as Technology Enthusiasts (TE), Technology Sceptics (TS) and Technology Pragmatists (TP). The obtained clusters were found to differ in terms of perception, attitude, intention, behavior, marital status, education, occupation and income levels. With respect to each cluster, it was seen that the top three important constructs are Perceived Usefulness (PU), Security (SEC) and Lifestyle Compatibility (LC) as indicated by the IPMA. The findings indicate that mobile wallet providers should focus on all six constructs, with special focus on PU, SEC and LC. The findings of this study will help mobile wallet providers in customizing their offerings to enhance adoption attitude in all three clusters.

Research limitations/implications

This study examines the perception of students and working professional towards mobile wallet adoption and uses this data for segmentation. However, there could be underlying differences between these two groups, as the motive behind adopting a technology may be different. Thus, treating them as homogenous user segments could be a limitation. Therefore, exploring segments and profiles for each type of user may be an area for future research. Mobile wallet providers should also give utmost importance to perceived usefulness, security and lifestyle compatibility while designing their services. This will not only enhance user trust and compatibility with mobile wallet but also improve the outcomes associated with its usage.

Practical implications

This study will help mobile wallet providers understand the user segments and customize their service offerings.

Originality/value

This study provides a comparison of the respondent profiles of three obtained segments of mobile wallet users. While prior studies have identified segments associated with adoption of technologies like ATM banking, SMS banking, online banking, Internet banking, mobile banking etc., not much has been reported on mobile wallet adoption. To the best of the authors' knowledge, this is a novel study in India, aimed at identifying user clusters among adopters of mobile wallets and developing cluster profiles based on demographic, attitude and intention.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 20 September 2021

Van Huy Nguyen

The purpose of this study is to employ social exchange and social representation theories to explain Kinh and Ethnic minorities’ perceptions toward tourism development in…

Abstract

Purpose

The purpose of this study is to employ social exchange and social representation theories to explain Kinh and Ethnic minorities’ perceptions toward tourism development in Sapa. A cluster analysis is used to segment their perceptions based on tourism impacts.

Design/methodology/approach

The primary data collection involved a survey with local residents in Sapa, Vietnam.

Findings

The results from cluster analysis separate 357 local residents into three clusters which are supporters, pessimists and neutralists. The supportive cluster comprises mainly young, female and less-educated respondents who support tourism development because of their employment and income; however, the pessimistic cluster which mostly consists of highly educated and elder respondents show more concerns about tourism development. Demographic profiles of respondents are classified in each cluster, so that policymakers can put forward specific policy for each ethnic group.

Research limitations/implications

The main limitation of this study is the high rate of incomplete responses in the questionnaires from ethnic minority groups.

Practical implications

Based on the findings of the study, implications are made for tourism planners and policymakers toward a future of more sustainable tourism development in the target context.

Originality/value

To the best of the author’s knowledge, this is the first study to segment the perceptions of Kinh and Ethnic minority groups toward tourism impacts in Sapa, Vietnam.

Details

International Journal of Tourism Cities, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2056-5607

Keywords

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Article
Publication date: 5 August 2021

Farzad Kiani, Amir Seyyedabbasi and Sajjad Nematzadeh

Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one…

Abstract

Purpose

Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues.

Design/methodology/approach

This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station.

Findings

The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results.

Originality/value

This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

Details

Sensor Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 10 August 2021

Elham Amirizadeh and Reza Boostani

The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show…

Abstract

Purpose

The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.

Design/methodology/approach

In data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback–Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder + k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.

Findings

First of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed.

Originality/value

Little works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

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