Author(s)

Huang Yitai ¹, Zhong Zeheng ², Fang Zhaoyang ³

Affiliation(s)

¹ School of Automotive Engineering, Wuhan University of Technology, Wuhan, China
² School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
³ School of Science, Wuhan University of Technology, Wuhan, China

Corresponding Author

Huang Yitai

ABSTRACT

Wordle is an interesting puzzle that gained huge popularity in early 2022. Studying the game's play data is important for its development and promotion. That's why our team has conducted research in this area. Firstly, we used Prophet Model to explain the reasons for the change in the number of reports and to predict the interval of change in the number of reports on a certain day in the future. Then, based on the word attributes that were tested by the Apriori Model, we use Spectral Clustering Model to classify the word by difficulty. With the above model, we obtained the results with good reliability and interpretability.

KEYWORDS

Wordle, Prophet, Apriori, Spectral Clustering

CITE THIS PAPER

Huang Yitai, Zhong Zeheng, Fang Zhaoyang, Prediction and Classification Model Based on Wordle's Date. Advances in Computer, Signals and Systems (2023) Vol. 7: 113-118. DOI: http://dx.doi.org/10.23977/acss.2023.070516.

REFERENCES

[1] Anderson, Benton J., and Jesse G. Meyer. "Finding the optimal human strategy for wordle using maximum correct letter probabilities and reinforcement learning." arXiv preprintarXiv: 2202.00557 (2022).
[2] Stewart, Jeffrey, et al. "The relationship between word difficulty and frequency: A response to Hashimoto (2021)." Language Assessment Quarterly 19.1 (2022): 90-101.
[3] Kim Kwang Hyeon, Byung-Jou Lee, and Hae-Won Koo. "Analysis of the Risk Factors for De Novo Subdural Hygroma in Patients with Traumatic Brain Injury Using Predictive Modeling and Association Rule Mining." Applied Sciences 13.3 (2023): 1243
[4] Cong Yi. "Research on data association rules mining method based on improved apriori algorithm." 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020: 373-376.
[5] Lei Jing, and Alessandro Rinaldo. "Consistency of spectral clustering in stochastic block models." (2015): 215-237.
[6] Bianchi Filippo Maria, Daniele Grattarola, and Cesare Alippi. "Spectral clustering with graph neural networks for graph pooling." International conference on machine learning. PMLR, 2020: 874-883.

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