The Categorising Characteristics of Facebook Pages: Using the K-Means Grouping Method

Luarn, Pin; Lin, Hong-Wen; Chiu, Yu Ping; Shyu, Yu-Liang; Lee, Pei-Ching

doi:10.5539/ijbm.v11n2p60

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Centroids basically denote the central-most point of a cluster. K-means algorithm has the advantage of low computational complexity and has severed as a good blueprint for the development of numerous clustering algorithms [14][15] [16] [17]. K-Means, however, is very sensitive to noise also known as outliers.…”

Section: Literature Reviewmentioning

confidence: 99%

A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

Takyi¹,

Bagga²,

Gupta³

2019

IJITEE

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The task of network administrators to identify and determine the type of traffic traversing through the network is very critical to the rapid growth of new traffic each day. As the requirements of networks change over time, the situation of the network not able to meet some requirements is likely to occur. In a wide area network with a limited resource such as the low speed of links, frequent fragmentation of packets leading to extreme packet loss and costs is prominent resulting in the poor quality of service. As a result quantified amount of traffic flows can be classified at a time with limited features lowering the effectiveness of traffic classification. To improve upon the classification in such scenarios, we propose a hybrid semi-supervised clustering that is able to classify packet flows with restricted features and a small amount of packets while maintaining high accuracy in classification. We implement the above scenario in simulation and classify the limited flows obtained with our proposed algorithm. Evaluation results show that our proposed algorithm implemented into a classifier has good accuracy and precision values, with low processing time and error rates. The proposed strategy will enable network administrators during times of network resource depletion or upgrades provide and ensure the best quality of services and identify unwanted or malicious traffic.

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Section: Literature Reviewmentioning

confidence: 99%

A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

Takyi¹,

Bagga²,

Gupta³

2019

IJITEE

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Business competitive analysis using promoted post detection on social media

Arora

Srivastava²,

Bansal

2020

Journal of Retailing and Consumer Services

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Application of Data Mining Technology under K-means Algorithm Combined with BIM Technology in Management Engineering

Wang

Zhan

2020

International Journal of Advanced Information and Communication Technology

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The data mining technology of the K-means algorithm combined with BIM (Building Information Modeling) technology is applied to management engineering, which is convenient for project management personnel. Method: The K-means clustering algorithm is combined with the support vector machine algorithm. The support vector machine is used to ensure the high accuracy of the anomaly detection algorithm. The K-means clustering algorithm is used to divide the support vector machine into blocks. It also analyzes the different needs of the facility management staff, and clearly defines the content and level of detail required to build the BIM model. It not only meets the data requirements for operation and maintenance but also avoids waste caused by excessive modeling. Result: Compared with traditional support vector machines, the improved algorithm in this paper has a higher detection rate and lower false alarm rate. Also, it can shorten the detection time of large-scale data to provide an effective method for abnormal detection of sensor networks and processing of large-scale data sets. The improved method increases the detection accuracy by 8.13% and decreases the false alarm rate by 89.08%. In terms of detection time, the improved method increases by 3.82s, which is 4.67 times the traditional method. Conclusion: The structural health monitoring system can efficiently and accurately monitor the accuracy of the data. BIM can provide rich operation and maintenance data for facility management to effectively improve the efficiency of facility management.

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The Categorising Characteristics of Facebook Pages: Using the K-Means Grouping Method

Cited by 3 publications

References 27 publications

A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

Business competitive analysis using promoted post detection on social media

Application of Data Mining Technology under K-means Algorithm Combined with BIM Technology in Management Engineering

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