TPE-AutoClust: A Tree-based Pipline Ensemble Framework for Automated Clustering

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@InProceedings{ElShawi:2022:ICDMW,

• author = "Radwa ElShawi and Sherif Sakr",
• booktitle = "2022 IEEE International Conference on Data Mining Workshops (ICDMW)",
• title = "{TPE-AutoClust:} A Tree-based Pipline Ensemble Framework for Automated Clustering",
• year = "2022",
• pages = "1144--1153",
• abstract = "Novel technologies in automated machine learning ease the complexity of building well-performed machine learning pipelines. However, these are usually restricted to supervised learning tasks such as classification and regression, while unsu-pervised learning, particularly clustering, remains a largely un-explored problem due to the ambiguity involved when evaluating the clustering solutions. Motivated by this shortcoming, in this paper, we introduce TPE-AutoClust, a genetic programming-based automated machine learning framework for clustering. TPE-AutoCl ust optimizes a series of feature preprocessors and machine learning models to optimize the performance on an unsupervised clustering task. TPE-AutoClust mainly consists of three main phases: meta-learning phase, optimization phase and clustering ensemble construction phase. The meta-learning phase suggests some instantiations of pipelines that are likely to perform well on a new dataset. These pipelines are used to warm start the optimization phase that adopts a multi-objective optimization technique to select pipelines based on the Pareto front of the trade-off between the pipeline length and performance. The ensemble construction phase develops a collaborative mechanism based on a clustering ensemble to combine optimized pipelines based on different internal cluster validity indices and construct a well-performing solution for a new dataset. The proposed framework is based on scikit-learn with 4 preprocessors and 6 clustering algorithms. Extensive experiments are conducted on 27 real and synthetic benchmark datasets to validate the superiority of TPE-AutoCl ust. The results show that TPE-AutoClust outperforms the state-of-the-art techniques for building automated clustering solutions.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICDMW58026.2022.00149",
• ISSN = "2375-9259",
• month = nov,
• notes = "Also known as \cite{10031132}",

}

Genetic Programming entries for Radwa ElShawi Sherif Sakr

Citations