Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

Yan-fang, Gao; Hu, Yanxue; Chu, Yuying

doi:10.1155/2023/7114343

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The k-modes algorithm employs novel dissimilarity metrics to handle categorical entities and employs a frequency-based approach to revise the cluster modes. According to Gao, Hu, and Chu [7], the k-prototypes algorithm is capable of handling a combination of both numerical and categorical data.…”

Section: Cluster Analysis K-prototypes K-modes and K-meansmentioning

confidence: 99%

A Review of Data Mining, Big Data Analytics and Machine Learning Approaches

Pedro

2023

JCNS

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The phenomenon of economic globalization has led to the swift advancement of industries across diverse domains. Consequently, big data technology has garnered increasing interest. The generation of network data is occurring at an unparalleled pace, necessitating the intelligent processing of vast amounts of data. To fully leverage the value inherent in this data, the implementation of machine learning techniques is imperative. The objective of machine learning in a vast data setting is to identify particular rules that are concealed within dynamic, variable, multi-origin heterogeneous data, with the ultimate aim of maximizing the value of the data. The integration of big data technology and machine learning algorithms is imperative in order to identify pertinent correlations within intricate and dynamic datasets. Subsequently, computer-based data mining can be utilized to extract valuable research insights. The present study undertakes an analysis of deep learning in comparison to conventional data mining and machine learning techniques. It conducts a comparative assessment of the strengths and limitations of the traditional methods. Additionally, the study introduces the requirements of enterprises, their systems and data, the IT challenges they face, and the role of Big Data in an extended service infrastructure. This study presents an analysis of the probability and issues associated with the utilization of deep learning, including machine learning and traditional data mining techniques, in the big data analytics context.

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Section: Cluster Analysis K-prototypes K-modes and K-meansmentioning

confidence: 99%

A Review of Data Mining, Big Data Analytics and Machine Learning Approaches

Pedro

2023

JCNS

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“…This study proposes the use of the K-prototype algorithm for clustering crashes on mountainous freeways. The K-prototype algorithm, first published by [24] , is a widely used ensemble clustering algorithm in various fields such as education [25] , sociology [26] , biology science [27] , and public safety [28] .…”

Section: Introductionmentioning

confidence: 99%

Crash data mining in mountainous freeway: a case study with K-prototype algorithm

Wang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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To enhance the risk management strategies for crashes in mountainous freeways, this study conducted a data mining using the mountainous freeway in Fuzhou as an example. The K-prototype algorithm was used to cluster crash types into three categories with a SSE of 6450.049. The three categories reflects different crash characteristics, which can be used to implement different strategies. C1 represents crashes with high death rates, primarily occurring in confluence zones and tunnels during holidays with high traffic flow. C2 represents crashes with high injury rates, occurring on bridges, long downhill slopes, and diversion zones during evening peak hours. C3 represents crashes with low severity but long rescue response times. Different risk management strategies such as visual guidance devices and recommended speed reminders, were proposed for the three types of crashes mentioned above. The result also showed that more attention should be paid to the peak hours and holiday time in critical areas to minimize the influence of mountainous freeway crashes.

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Research on Power System Load Clustering based on Signal Analysis and K-Prototypes

Peng,

Liu

2023

2023 3rd International Conference on Electrical Engineering and Control Science (IC2ECS)

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Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

Cited by 3 publications

References 19 publications

A Review of Data Mining, Big Data Analytics and Machine Learning Approaches

A Review of Data Mining, Big Data Analytics and Machine Learning Approaches

Crash data mining in mountainous freeway: a case study with K-prototype algorithm

Research on Power System Load Clustering based on Signal Analysis and K-Prototypes

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