Exploring meta-heuristics for partitional clustering: methods, metrics, datasets, and challenges

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arvinder Kaur, Yugal Kumar, Jagpreet Sidhu
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引用次数: 0

Abstract

Partitional clustering is a type of clustering that can organize the data into non-overlapping groups or clusters. This technique has diverse applications across the different various domains like image processing, pattern recognition, data mining, rule-based systems, customer segmentation, image segmentation, and anomaly detection, etc. Hence, this survey aims to identify the key concepts and approaches in partitional clustering. Further, it also highlights its widespread applicability including major advantages and challenges. Partitional clustering faces challenges like selecting the optimal number of clusters, local optima, sensitivity to initial centroids, etc. Therefore, this survey describes the clustering problems as partitional clustering, dynamic clustering, automatic clustering, and fuzzy clustering. The objective of this survey is to identify the meta-heuristic algorithms for the aforementioned clustering. Further, the meta-heuristic algorithms are also categorised into simple meta-heuristic algorithms, improved meta-heuristic algorithms, and hybrid meta-heuristic algorithms. Hence, this work also focuses on the adoption of new meta-heuristic algorithms, improving existing methods and novel techniques that enhance clustering performance and robustness, making partitional clustering a critical tool for data analysis and machine learning. This survey also highlights the different objective functions and benchmark datasets adopted for measuring the effectiveness of clustering algorithms. Before the literature survey, several research questions are formulated to ensure the effectiveness and efficiency of the survey such as what are the various meta-heuristic techniques available for clustering problems? How to handle automatic data clustering? What are the main reasons for hybridizing clustering algorithms? The survey identifies shortcomings associated with existing algorithms and clustering problems and highlights the active area of research in the clustering field to overcome these limitations and improve performance.

Abstract Image

探索分区聚类的元启发式方法:方法、度量、数据集和挑战
局部聚类是一种能将数据组织成不重叠的组或簇的聚类技术。这种技术在图像处理、模式识别、数据挖掘、基于规则的系统、客户细分、图像细分和异常检测等不同领域有着广泛的应用。因此,本调查旨在确定分区聚类的关键概念和方法。此外,它还强调了其广泛的适用性,包括主要优势和挑战。分区聚类面临着选择最佳聚类数量、局部最优、对初始中心点的敏感性等挑战。因此,本调查将聚类问题描述为局部聚类、动态聚类、自动聚类和模糊聚类。本调查的目的是确定上述聚类的元启发式算法。此外,元启发式算法还分为简单元启发式算法、改进元启发式算法和混合元启发式算法。因此,这项工作还侧重于采用新的元启发式算法,改进现有方法和新技术,以提高聚类性能和鲁棒性,使分区聚类成为数据分析和机器学习的重要工具。本调查还重点介绍了用于衡量聚类算法有效性的不同目标函数和基准数据集。在进行文献调查之前,我们提出了几个研究问题,以确保调查的有效性和效率,例如有哪些可用于聚类问题的元启发式技术?如何处理自动数据聚类?混合聚类算法的主要原因是什么?调查指出了与现有算法和聚类问题相关的不足之处,并强调了聚类领域为克服这些局限性和提高性能而积极开展的研究领域。
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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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