Clustering Analysis of Borrowing Data of University Library based on K-means Algorithm

Li, Silin

doi:10.1109/icetci55101.2022.9832097

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Besides decision tree methods, clustering methods were frequently used in most research. In [5], Kmeans algorithm was used to cluster the borrowing books data and analysed the clustering characteristics of readers and books. The category and sample count in the K-means clustering technique are often arbitrarily determined.…”

Section: The Role Of Machine Learning In Library Systemmentioning

confidence: 99%

Analysis of Book Preferences Among Visitors in Library System

Foo,

Ng,

Haw

et al. 2024

TEM Journal

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Library is a place that contains various resources and materials. Many invaluable knowledge can be found in the library. By analysing the library’s data, it is possible to obtain information that can further improve its services. This research aims to extract information from Multimedia University (MMU) library and present insightful visualization of the information to enhance the library administration. At present, the library does not have information on the book preferences of the library users. The book preferences statistics can be relatively helpful as the library will know what books can be imported in the future. By doing so, more people will visit the library and they will have more related books to use as reference or to read. In addition, there are no existing dashboards to display information on all borrowers, no visitor. In the absence of this, this research adopts the data science methodology to determine the book preferences of library users by using machine learning techniques such as clustering and classification. Lastly, a dashboard will be developed to display all the findings which includes statistics on the visitors and book preferences.

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Section: The Role Of Machine Learning In Library Systemmentioning

confidence: 99%

Analysis of Book Preferences Among Visitors in Library System

Foo,

Ng,

Haw

et al. 2024

TEM Journal

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“…Nie Xiaowei [7] and others used the K-prototype algorithm to cluster mixed-feature data and construct user profiles. Li Wei [8] and others introduced a multi-view bi-partition K-means algorithm based on Mahalanobis distance, solving the issues posed by the influence of attribute dimensions in multi-view scenarios using Euclidean distance. Han Lu [9] and others designed a K-means clustering algorithm based on Sugeno set order, reconstructing the dynamic clustering algorithm of K-means.…”

Section: User Profilingmentioning

confidence: 99%

Cluster-Based Federated Learning for Automotive Customer Profile Analysis

Cui

2024

AEMPS

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In the context of increasingly fierce competition in the automotive market, car dealerships are shifting from a "product-oriented" approach to a "customer-oriented" one. This paper establishes a customer profile tagging system based on car purchasing behaviors and customer attributes, and constructs a customer profile model through cluster analysis. However, traditional machine learning algorithms face limitations such as insufficient data, single sources, and incomplete tags. Additionally, data cannot be directly shared due to data security concerns. Therefore, this paper applies a FederatedKMeans algorithm, which combines federated learning and K-means clustering, to construct profiles. The superiority of this algorithm in terms of performance is demonstrated through empirical tests, and it is used for cluster analysis to categorize customers into different groups, providing corresponding marketing strategies.

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Clustering Analysis of Borrowing Data of University Library based on K-means Algorithm

Cited by 2 publications

References 3 publications

Analysis of Book Preferences Among Visitors in Library System

Analysis of Book Preferences Among Visitors in Library System

Cluster-Based Federated Learning for Automotive Customer Profile Analysis

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