Search results
1 – 10 of over 18000Jie Ma, Zhiyuan Hao and Mo Hu
The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and…
Abstract
Purpose
The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.
Design/methodology/approach
First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.
Findings
The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.
Originality/value
The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.
Details
Keywords
Jie Zhu, Jing Yang, Shaoning Di, Jiazhu Zheng and Leying Zhang
The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information…
Abstract
Purpose
The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.
Design/methodology/approach
In this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.
Findings
The effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.
Originality/value
Traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.
Details
Keywords
Yu‐Min Su, Ping‐Yu Hsu and Ning‐Yao Pai
The co‐word analysis method is commonly used to cluster‐related keywords into the same keyword domain. In other words, traditional co‐word analysis cannot cluster the same…
Abstract
Purpose
The co‐word analysis method is commonly used to cluster‐related keywords into the same keyword domain. In other words, traditional co‐word analysis cannot cluster the same keywords into more than one keyword domain, and disregards the multi‐domain property of keywords. The purpose of this paper is to propose an innovative keyword co‐citation approach called “Complete Keyword Pair (CKP) method”, which groups complete keyword sets of reference papers into clusters, and thus finds keywords belonging to more than one keyword domain, namely bridge‐keywords.
Design/methodology/approach
The approach regards complete author keywords of a paper as a complete keyword set to compute the relations among keywords. Any two complete keyword sets whose corresponding papers are co‐referenced by the same paper are recorded as a CKP. A clustering method is performed with the correlation matrix computed from the frequency counts of the CKPs, for clustering the complete keyword sets. Since keywords may be involved in more than one complete keyword set, the same keywords may end up appearing in different clusters.
Findings
Results of this study show that the CKP method can discover bridge‐keywords with average precision of 80 per cent in the Journal of the Association for Computing Machinery citation bank during 2000‐2006 when compared against the benchmark of Association for Computing Machinery Computing Classification System.
Originality/value
Traditional co‐word analysis focuses on co‐occurrence of keywords, and therefore, cannot cluster the same keywords into more than one keyword domain. The CKP approach considers complete author keyword sets of reference papers to discover bridge‐keywords. Therefore, the keyword recommendation system based on CKP can recommend keywords across multiple keyword domains via the bridge‐keywords.
Details
Keywords
Wei Zhang, Xianghong Hua, Kegen Yu, Weining Qiu, Xin Chang, Bang Wu and Xijiang Chen
Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve…
Abstract
Purpose
Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.
Design/methodology/approach
The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.
Findings
Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.
Research limitations/implications
Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.
Practical implications
The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.
Social implications
The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.
Originality/value
A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.
Details
Keywords
Claudia Vásquez Rojas, Eduardo Roldán Reyes, Fernando Aguirre y Hernández and Guillermo Cortés Robles
Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and…
Abstract
Purpose
Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and methods are manually applied and may be time consuming. The purpose of this paper is to introduce an automatic method to define strategic plans by using text mining (TM) algorithms within a generic SP model especially suited for small- and medium-sized enterprises (SMEs).
Design/methodology/approach
Textual feedbacks were collected through a SWOT matrix during the implementation of a SP model in a company dedicated to the local distribution of food. A four-step TM process (performing acquisition, pre-processing, processing, and validation tasks) is applied via a framework developed under the cloud computer paradigm in order to determine the strategic plans.
Findings
The use of categorization and clustering algorithms show that unstructured textual information produced during the SP can be efficiently processed and capitalized. Collected evidence reveals the potential to enhance the strategic plans creation with less effort and time, improving the relevance, and producing new technological resources accessible to SMEs.
Originality/value
An innovative framework especially suited for the SMEs based on the synergy assumption of the coupling between TM and a generic SP model.
Details
Keywords
Surachai Chancharat and Arisa Phadungviang
This study groups mutual funds using k-means clustering analysis and compares the k-means clustering process with existing clustering techniques using mutual fund data for equity…
Abstract
This study groups mutual funds using k-means clustering analysis and compares the k-means clustering process with existing clustering techniques using mutual fund data for equity funds, general fixed-income funds, and balanced open-end mutual funds rated by the Association of Investment Management Companies. Data are from January 2016 to December 2020 for 60 months and includes information on prices, risks, and investment policies. The sample for this study comprises 173 funds from 10 asset management companies with the highest net assets. The tool used for analysis is the k-means technique using a statistical package set for k = 3. The funds can be divided into three groups: Group 1 has 5 mutual funds (2.89%), Group 2 has 24 mutual funds (13.87%), and Group 3 has a total of 144 mutual funds (83.24%). In Group 1, four of the five mutual funds are equity funds with a track record of beating the market, and fund managers have good market timing skills. Moreover, the efficiency of fund grouping using the k-means technique was compared with the existing grouping with close results at 57.23%. This work provides a methodology to obtain a better categorization of mutual funds by using k-means clustering, allowing the investors to know how mutual funds are. This categorization is very useful for improving the formulation of mutual funds, with the goal of further optimizing investment.
Details
Keywords
Nicola Castellano, Roberto Del Gobbo and Lorenzo Leto
The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on…
Abstract
Purpose
The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.
Design/methodology/approach
The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.
Findings
The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.
Practical implications
The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.
Originality/value
This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.
Details
Keywords
Sudhir Rana and Somesh Kr. Sharma
This study examines the conceptual domain of international marketing following substantial growth in its development. With the objective to investigate recent patterns and…
Abstract
This study examines the conceptual domain of international marketing following substantial growth in its development. With the objective to investigate recent patterns and development in the literature this study evaluates 1,816 research articles on international marketing published between 1990 and 2012. The classification of conceptual domain has yielded 57 configurational contents under seven prime research streams. Simple meta-analysis on international marketing literature created a clear depiction of attention of contributors toward research streams and the number of contributors, and worthy sources of literature. Several directions for advancement of knowledge in international marketing, identified fields, and their implications for future research are discussed.
Details
Keywords
Xi Chen, Zuohao Hu, Xuanzhong Sun and Ping Zhao
The purpose of this paper is to identify the typology of Chinese indigenous exporters by incorporating proactive‐reactive and long‐ and short‐term export motivations as inputs…
Abstract
Purpose
The purpose of this paper is to identify the typology of Chinese indigenous exporters by incorporating proactive‐reactive and long‐ and short‐term export motivations as inputs. This study also seeks to find out whether, when driven with a different strength of four export motives, firms differ significantly in terms of commitment, learning, competence and performance.
Design/methodology/approach
This paper employs cluster analysis to explore the typology of Chinese exporters and conducts ANOVA to compare subsequent differences in organizational characteristics, competence and performance. Case studies are then used to validate and exemplify the typology.
Findings
Findings suggest that Chinese exporters fall into four segments: the prospector, the strategist, the hesitator and the experimentalist. Each shows a unique set of organizational characteristics and different performances. The prospector is most competitive and the best performer, followed by the strategist.
Research limitations/implications
The study uses limited export motives and profiling variables to understand this in a static way. Other motives and profiling variables are welcomed, and future study can address this in a dynamic way.
Practical implications
The findings suggest an evolutionary path for exporters and implies how to strengthen proactive and long‐term motives in order to achieve superior performance.
Originality/value
This paper for the first time looks at firms that are already involved in exporting, how differently they are motivated and how their initial internationalization motivations lead to sharp differences in export performance.
Details
Keywords
Boris Inkizhinov, Elena Gorenskaia, Dashi Nazarov and Anton Klarin
To provide a comprehensive systematic review of entrepreneurship in the context of emerging markets (EMs). The area of research is topical considering the rise of EMs on the…
Abstract
Purpose
To provide a comprehensive systematic review of entrepreneurship in the context of emerging markets (EMs). The area of research is topical considering the rise of EMs on the global scene and the importance of entrepreneurship in the development of EMs.
Design/methodology/approach
The paper utilizes scientometrics to provide a systematic review of the emerging field of entrepreneurship in EMs (EEMs). The entire Web of Science database was searched, and 2,568 scholarly outputs were extracted and analyzed as a result. The review further compares the EEMs research to the mainstream entrepreneurship research based on the top trending and high impact themes, demonstrates which countries published and are studied in the EEMs scholarship, and finally, it provides a proportion of empirical research done on EEMs to highlight methods utilized in the existing research.
Findings
The scientometric review reveals three broad domains of the EEMs scholarship–(1) Entrepreneurship in EMs and its implications; (2) MNEs, institutional environments, and FDI; and (3) Strategy, innovation and performance. The findings demonstrate that EEMs' scholarship primarily discusses environments within which EEMs takes place, the implications of EEMs, strategy and performance of EEMs (macro and meso-levels), thus highlighting the need for micro-level (individual-based) analysis of EEMs. Approximately, a third of the EEMs research is of empirical nature, more should be done especially in quantitative studies to develop this field further.
Originality/value
This research is unique in providing the largest review of EEMs scholarship. It divides the entire scholarship into three inter-related research streams and identifies future research directions in this immensely important field of research.
Details