An Improved Clustering Algorithm and Its Application in WeChat Sports Users Analysis

Yao, Xuanxia; Ge, S. P.; Kong, Huafeng; Ning, Huansheng

doi:10.1016/j.procs.2018.03.067

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…The main difference between WeChat and other social media services like Facebook and Twitter is that WeChat users know most of their contacts personally [11]. In the past few years, WeChat's embedded payment service (WeChat Pay, one of the largest online payment platforms in China) has further integrated it into users' lives from various aspects, such as tourism [12], sports [13], marketing [11], public engagement [14], and even medical care [15]. To share information, WeChat users can post texts, images, and short audios/videos on "moments", literally known as "friend space" ("朋友圈" in Chinese, similar to "homepage"), or send them directly to individual contacts or chat groups using the messaging function.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of the Influencing Factors of Chinese WeChat Users’ Environmental Information-Sharing Behavior Based on an Integrated Model of UGT, NAM, and TPB

Chen

2020

Sustainability

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Sustainable development is a common challenge for all. Under this background, how to promote public participation in environmental communication has become an important topic. The purpose of this study is to understand the motivating mechanism behind Chinese WeChat users’ environmental information-sharing behavior by taking China’s unique social and cultural background into account. A comprehensive theoretical model for this study is constructed based on the uses and gratification theory, the norm activation model, and the theory of planned behavior. Through an online survey, data were collected from 526 participants to test the research model. The research results show that Chinese WeChat users’ environmental information-sharing behavior is motivated by both egoistic factors (self-presentation, information seeking, and socializing) and altruistic factors (awareness of consequences and ascription of responsibility). During the behavioral decision-making process, these motivating factors impact people’s actual sharing behavior via their attitudes toward the behavior, subjective norms, personal norms, and behavioral intention in various patterns. The findings are discussed from an interdisciplinary perspective of media usage, prosocial behavior, and behavioral psychology. This article not only proposes a new conceptual framework to explain social media users’ behavior of sharing environmental information but also provides important theoretical and practical implications regarding motivating public participation in environmental communication on social media.

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Section: Introductionmentioning

confidence: 99%

An Investigation of the Influencing Factors of Chinese WeChat Users’ Environmental Information-Sharing Behavior Based on an Integrated Model of UGT, NAM, and TPB

Chen

2020

Sustainability

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“…These datasets have predefined classes and the number of the classes is taken as the number of clusters in the datasets. Yao et al [39] extend the algorithm [38] by adding a method to find the initial clusters to avoid the cluster initialization problem. However, the method to find initial clusters is based on density estimation which makes the method quadratic.…”

Section: ) Number Of Clustersmentioning

confidence: 99%

Survey of State-of-the-Art Mixed Data Clustering Algorithms

2019

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Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field. INDEX TERMS Categorical features, clustering, mixed datasets, numeric features. I. INTRODUCTION Clustering is an unsupervised machine learning technique used to group unlabeled data into clusters that contain data points that are 'similar' to each other and 'dissimilar' from those in other clusters [1], [2]. Many clustering algorithms can only handle data that contain either numeric or categorical feature values [3], [4]. Numeric features can take real values, such as height, weight, and distance. Categorical features represent data that can be divided into a fixed number of categories, such as color, race, sex, profession, and blood group. Clustering algorithms group data points into clusters using some notion of 'similarity', which can be as simple as the Euclidean distance. To compute the similarity between numeric feature values, mathematical operations (such as distances, angles, summation, or mean) are applied to them. Distance-based similarity measures are mostly used for numeric data points. Generally, categorical feature values are not inherently ordered (for example, the categorical values, red and blue). It is not possible to directly compute the distance between two categorical feature values. Therefore, computing distance-based similarity measures for categorical data is a challenging task [5]. Nevertheless, several methods The associate editor coordinating the review of this manuscript and approving it for publication was Haruna Chiroma.

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“…Clustering categorizes the objects convincingly and obtains the unknown samples that may be present in the datasets [32]. As a result, the focus of today's research revolve more or less on different clustering approaches with an effort towardsimproving the structure and pattern of superior cluster conception [2,29,33], curtailing noise from data present in clusters [15,34,39], deciding near optimal number of centroids [21,36] and initiating cluster as an approach in a variety of domains.…”

Section: Introductionmentioning

confidence: 99%

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Mishra¹,

Rath²,

Nanda³

et al. 2019

IJISA

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At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them. When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria. This is because an inefficient center may result in unpredicted outcomes. Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers. We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure. They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices. The clustering accuracy was also conducted using Rand index criteria. All the algorithms computational complexity was analyzed and finally their computation time was also recorded. As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers. More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.

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An Improved Clustering Algorithm and Its Application in WeChat Sports Users Analysis

Cited by 9 publications

References 7 publications

An Investigation of the Influencing Factors of Chinese WeChat Users’ Environmental Information-Sharing Behavior Based on an Integrated Model of UGT, NAM, and TPB

An Investigation of the Influencing Factors of Chinese WeChat Users’ Environmental Information-Sharing Behavior Based on an Integrated Model of UGT, NAM, and TPB

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Efficient Intelligent Framework for Selection of Initial Cluster Centers

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