Abstract
Clustering is an important means of obtaining hidden information, and is widely used in economics, biomedicine and other disciplines. Data imbalance widely exists in real-world datasets. For example, when fraud detection is performs in transaction data, only a very small amount of transaction data has fraudulent behavior. Therefore clustering on density-imbalanced datasets has practical implications. Various clustering algorithms have been proposed in recent years, but most clustering algorithms cannot correctly identify low-density clusters on density-imbalanced datasets, resulting in clustering failure. To this end, we propose a density ratio peak clustering (DRPC) algorithm, which solves the problem that the original density peak clustering (DPC) algorithm cannot correctly identify low-density clusters and non-center points allocation error linkage problem on density-imbalanced datasets. We conduct experiments on shape datasets, density-imbalanced datasets, and UCI real-world datasets, using normalized mutual information NMI as the evaluation metric, comparing with SNN-DPC, DPC-KNN, DPC, DBSCAN, K-Means algorithms. Experiment results show that DRPC not only inherits the advantages of DPC, but also can more accurately cluster density-imbalanced datasets, and the NMI of the clustering results has increased by \(1.5\%\) on average.
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References
Fang, U., Li, J., Akhtar, N., Li, M., Jia, Y.: Gomic: multi-view image clustering via self-supervised contrastive heterogeneous graph co-learning. World Wide Web-Internet Web Inf. Syst. (2023)
Wang, J., Shi, Y., Li, D., Zhang, K., Chen, Z., Li, H.: MCHA: a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation. World Wide Web-Internet and Web Inf. Syst. 25(3, SI), 1103–1127 (2022)
Yuan, C., Zhu, Y., Zhong, Z., Zheng, W., Zhu, X.: Robust self-tuning multi-view clustering. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 25(2, SI), 489–512 (MAR 2022)
Wang, H.Z.: Corrigendum to ‘a fuzzy k-prototype clustering algorithm for mixed numeric and categorical data. Knowl.-Based Syst. 30, 129–135 (2012)
Tian, Z., Ramakrishnan, R.: Miron livny: birch: an efficient data clustering method for very large databases. ACM SIGMOD Rec. 25(2) (1999)
Wang, W., Yang, J., Muntz, R.: Sting: A statistical information grid approach to spatial data mining. In: VLDB’97, Proceedings of 23rd International Conference on Very Large Data Bases, 25–29 August 1997, Athens, Greece (1997)
Cai, J., Luo, J., Wang, S., Yang, S.: Feature selection in machine learning: a new perspective. Neurocomputing 300, 70–79 (2018)
Du, M., Ding, S., Jia, H.: Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl.-Based Syst. 99, 135–145 (2016)
Xiang, L.Y.H..: Dynamic resource allocation algorithm based on big data stream characteristic and improved SOM clustering. Comput. Appl. Softw. (2019)
Hu, T., Sung, S.Y.: A Hybrid EM Approach to Spatial Clustering. Elsevier Science Publishers B. V., Amsterdam (2006)
Pourbahrami, S., Hashemzadeh, M.: A geometric-based clustering method using natural neighbors. Inf. Sci. 610, 694–706 (2022)
Pourbahrami, S., Khanli, L.M., Azimpour, S.: Improving neighborhood construction with apollonius region algorithm based on density for clustering. Inf. Sci. 522, 227–240 (2020)
Xu, X., Ding, S., Wang, L., Wang, Y.: A robust density peaks clustering algorithm with density-sensitive similarity. Knowl.-Based Syst. 200 (2020)
Cheng, D., Zhang, S., Huang, J.: Dense members of local cores-based density peaks clustering algorithm. Knowl.-Based Syst. 193 (2020)
Tao, X., et al.: Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering. Inf. Sci. 519, 43–73 (2020)
Gong, C., Su, Z.G., Wang, P.H., Wang, Q.: Cumulative belief peaks evidential k-nearest neighbor clustering. Knowl.-Based Syst. 200 (2020)
Flores, K.G., Garza, S.E.: Density peaks clustering with gap-based automatic center detection. Knowl.-Based Syst. 206 (2020)
Lu, H., Shen, Z., Sang, X., Zhao, Q., Lu, J.: Community detection method using improved density peak clustering and nonnegative matrix factorization. Neurocomputing 415, 247–257 (2020)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Wang, S., Li, Q., Zhao, C., Zhu, X., Dai, T.: Extreme clustering - a clustering method via density extreme points. Inf. Sci. 542 (2020)
Cheng, D., Huang, J., Zhang, S., Liu, H.: Improved density peaks clustering based on shared-neighbors of local cores for manifold data sets. IEEE Access 7, 151339–151349 (2019)
Liu, R., Huang, W., Fei, Z., Wang, K., Liang, J.: Constraint-based clustering by fast search and find of density peaks. Neurocomputing 330, 223–237 (2019)
Liu, L., Yu, D.: Density peaks clustering algorithm based on weighted k-nearest neighbors and geodesic distance. IEEE Access 8, 168282–168296 (2020)
Li, J., Zhu, Q., Wu, Q.: A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor. Knowl.-Based Syst. 184 (2019)
Li, R., Yang, X., Qin, X., Zhu, W.: Local gap density for clustering high-dimensional data with varying densities. Knowl.-Based Syst. 184 (2019)
Wang, Y., Wong, K.C., Li, X.: Exploring high-throughput biomolecular data with multiobjective robust continuous clustering. Inf. Sci. 583, 239–265 (2022)
Tao, X., Chen, W., Zhang, X., Guo, W., Qi, L., Fan, Z.: SVDD boundary and DPC clustering technique-based oversampling approach for handling imbalanced and overlapped data. Knowl.-Based Syst. 234 (2021)
Dua, D., Newman, D.: UCI machine learning repository, University of California, School of Information and Computer Science (2017)
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This research is supported by National Natural Science Foundation of China (62306033, 62076027).
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Wang, S. et al. (2024). Density Ratio Peak Clustering. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_31
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DOI: https://doi.org/10.1007/978-981-97-2421-5_31
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