mCAF: a multi-dimensional clustering algorithm for friends of social network services

Chang, Hsien-Tsung; Li, Yuwen; Mishra, Nilamadhab

doi:10.1186/s40064-016-2420-1

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…The effect of similarity measurement highly depends on the user's comment information. So improving the reliability of similarity measurement can improve the reliability and accuracy of clustering [17].…”

Section: Two-dimensional Graph Modelmentioning

confidence: 99%

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GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

Sun

Yin

et al. 2019

IEEE Access

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As the continuous development of mobile social networks, the structure of the mobile social network increasingly becomes complex. It not only speeds up information transmission between people but also expands the scope of information exchange, which has become an essential and important social media in people's social life. How to effectively identify and classify these online communities has important practical significance for the study of social networks. Correctly detecting the community structure of mobile social networks can not only improve the accuracy of friend recommendation, link prediction, service user positioning, product marketing, and other aspects but also provide an important basis for the monitoring of online public opinion. But the traditional social network cluster method based on the trust degree mainly calculates the user trust by analyzing the interactive feedback information between users. This method cannot effectively solve the ''cold start'' problem in the trust calculation process, that is, for the new network node, the trust value of this node cannot be accurately measured due to the lack of interaction with other nodes. Focusing on this problem, we propose a Gaussian pigeon-oriented graph clustering algorithm for social networks' cluster in this paper. A graph model is first built. Then, an efficient K-medoid algorithm is utilized to search the user center in all groups. The Gaussian pigeon algorithm is used to search the similarity between each user and the central user. Users that meet the similarity threshold are divided into the same user group. Finally, the simulation results show that the proposed method has better cluster effect than other state-ofthe-art social networks' clustering approaches.INDEX TERMS Social network, Gaussian pigeon algorithm, graph clustering, K-medoid algorithm.

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Section: Two-dimensional Graph Modelmentioning

confidence: 99%

“…Therefore, in this paper, the distance between users and the clustering center is taken as the fitness function to evaluate the clustering results. The selection way of clustering center is shown in equation ( 16), and the fitness function is shown in equation (17).…”

Section: Gpogc: Gaussian Pigeon-oriented Graph Clustering Algorithmmentioning

confidence: 99%

GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

Sun

Yin

et al. 2019

IEEE Access

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“…KDD (Knowledge discovery on data) feats the way to integrate the data mining with the data analytics that makes the use of data science in numerous domain applications, such as business intelligence domain [12]. The machine learning becomes extremely important and useful in data science environment to deal not only objective with huge amounts of data and extract knowledge from it but also create trends in IoE big data analytics in increasing extensiveness with all levels of an organization.…”

Section: Context Of Data Science and Analyticsmentioning

confidence: 99%

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Mishra¹

2018

IJSRCSE

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Abstract-The latest internet of everything (IoE) advancements in data elicitation and digital storage technology leads to a large heterogeneous data depository, in which the IoE data are stored in a column oriented relational framework. The main purposes of the research are to design and explore data models, frameworks, architectures, and algorithms on network-centric data, mainly IoE data to accomplish the data science and knowledge analytic tasks for Intellectual domain applications. Some storage incompatibilities are there in the relational structure of multi-objective IoE data base that creates threats to data integrity and consistency. In a large scale IoE database, huge numbers of rows are there along with limited number of columns. So, column oriented relational framework greatly improve the performance of IoE data base in terms of data depository and access management. Knowledge analytic is the major part of data science; Analytic is a never ending process because of progressive technological change requirements as well as the business change requirements. The beauty of Analytics is that two data scientist with same problem may come up with two different new solutions. So, in this work, I discuss the overall data science and knowledge analytic streams for an effective IoE database management and knowledge discovery.

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“…Among the three applications, the highest degree of risk is measured in safety-critical IoE application. In business automation environment, IoE data science regulate several smart management tasks, such as, material logistic management, supplier chain management, product lifecycle management, compliance service work flow interoperations and management, proactive prediction of business security strategy, and much more [11]. The IoEA also regulates the data of wearable and non-wearable computing devices and generate intelligence through analytic systems to transform into a smart environment in order to monitor several activities, such as human activity supervision, automated coordination of devices according to human activity in a smart home like environment, monitoring traffic congestions, human activities, environmental pollutions, water pollutions, citizen compliance tracking, wastage management, intelligent transportations, and other activities and services in a smart city like environment.…”

Section: IImentioning

confidence: 99%

Reasonable e-Business expectancy Analytics in an IoE Edge

Mishra¹

2018

IJRASET

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Abstract:With the rapid growth of e-Business applications, several new analytics are researched for assessing the strategy that encompasses the current BI decisions, buying policies, consumer behaviour to e-business and vice versa. The consumer-end views can be composed from their registered IoE edge point devices that are exposed for assessing e-BI strategy. The consumerend views are considered as the potential data sources that need an effective cognitive analytics system to massively turn into the e-business goldmines. The Internet of Everything motivated consumer-end analytics has much brighter prospective in order to modernise the e-business intelligence applications. In this work, we discuss an intellectual consumer-end analytic system that implements an integrated choice based problem-solving mechanism to generate the insights for the predefined e-business parameters and dynamically suggests the solutions for the prospective BI requirements. For the system implementation, we in specta case base analysis that assesses the e-BI parameters copycatted with some real values. The analysis acts appositive elucidation in small scale e-business data depository to generate potential business worth and the small scale data repository may transform into a big data-value for the modernised e-BI applications. Keywords: IoE and Analytics, consumer-end analytics, BQ (business queries) knowledge base, IoE edge, e-BI I. INTRODUCTION In the current and upcoming days, the IoE tasks are gaining popularities across various Intellectual BI applications. The main aim is to get the insights from large scale network-centric data, such as IoE data that can be used to produce intelligence for the applications. In the current Intellectual domain applications, the network-centric data are highly unstructured and ambiguous, and create research challenges in inferencing the potential knowledge. The survey reveals that time to insight is slow, quality of insight is poor, and cost of insight is high for IoE big data applications, on the other hand, those Intellectual domain applications require low cost, high quality, and real time systems and algorithms to massively transform their data into cognitive values of goldmines. Such cognitive values are utilized as knowledge and insights for creating worth of the Intellectual domain applications [1][2][3][4]. The IoE (Internet of Everything) driven consumer-end analytics has much bright prospective in order to modernise the e-business intelligence applications. The global IoE evolutionary network capable to connect billions of IoE objects for the purpose of autonomous communication. In e-business intelligence applications, the IoE and analytics play major role towards generating the business insights that enable the e-business to construct a global trends and strategy for the e-consumers. The e-consumers are the real back bone of the e-BI operations. So, the consumer-end analytics are the powerful tools to analyse the current e-business trends and strategy and such analytics deeply consider th...

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mCAF: a multi-dimensional clustering algorithm for friends of social network services

Cited by 9 publications

References 21 publications

GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Reasonable e-Business expectancy Analytics in an IoE Edge

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