A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering

Aljarah, Ibrahim; Habib, Maria; Nujoom, Razan; Faris, Hossam; Mirjalili, Seyedali

doi:10.1007/978-981-33-4191-3_2

Cited by 7 publications

(3 citation statements)

References 171 publications

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“…To cluster the resulting Mapper graph, the scikit-learn implementation , of the spectral clustering method was used . The Calinski-Harabasz (CH) index was used as a quantitative metric for the clusters, again, using the scikit-learn implementation. , The CH score is an unsupervised clustering evaluation metric based on the Between-Cluster Dispersion (BCD) and the Within-Cluster Dispersion (WCD), which can be calculated as follows: BCD = ∑ k = 1 K n k false| c k − c false| 2 K − 1 WCD = ∑ k = 1 K ∑ i = 1 n k false| d i − c k false| 2 N − K CH = BCD WCD …”

Section: Methodsmentioning

confidence: 99%

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis

Derwae,

Nijs,

Geysels

et al. 2023

Anal. Chem.

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Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of molecules in tissues. An MSI experiment results in large amounts of high dimensional data, so efficient computational methods are needed to analyze the output. Topological Data Analysis (TDA) has proven to be effective in all kinds of applications. TDA focuses on the topology of the data in high dimensional space. Looking at the shape in a high dimensional data set can lead to new or different insights. In this work, we investigate the use of Mapper, a form of TDA, applied on MSI data. Mapper is used to find data clusters inside two healthy mouse pancreas data sets. The results are compared to previous work using UMAP for MSI data analysis on the same data sets. This work finds that the proposed technique discovers the same clusters in the data as UMAP and is also able to uncover new clusters, such as an additional ring structure inside the pancreatic islets and a better defined cluster containing blood vessels. The technique can be used for a large variety of data types and sizes and can be optimized for specific applications. It is also computationally similar to UMAP for clustering. Mapper is a very interesting method, especially its use in biomedical applications.

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Section: Methodsmentioning

confidence: 99%

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis

Derwae,

Nijs,

Geysels

et al. 2023

Anal. Chem.

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“…Although no consensus exists regarding those properties, most definitions agree that the fundamental characteristics of clusters are homogeneity (compactness) within and heterogeneity (separation) across groups. Many clustering criteria have been proposed [3], [4], each evaluating, in particular ways, one of these properties or a combination of them. It is unlikely, however, that a single solution can simultaneously optimize all the desirable, but usually conflicting, characteristics [5].…”

Section: A Multi-objective Clusteringmentioning

confidence: 99%

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Garza-Fabre

Handl

José-García

2023

IEEE Trans. Evol. Computat.

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Multi-view data analysis provides an effective means to integrate the distinct information sources which are inherent to many applications. Data clustering in a multi-view setting specifically aims to identify the most appropriate grouping for a collection of entities, where those entities (or their relationships) can be described from multiple perspectives. Leveraging recent advances in multi-objective clustering, we propose a new evolutionary method to tackle this challenge. Designed around a flexible and unbiased solution representation, together with strategies based on the minimum spanning tree and neighborhood relations, our algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate trade-offs between the data views. Through a series of experiments, we investigate the suitability of our proposal in the context of a bioinformatics application, clustering of plausible protein structures, and a diverse set of synthetic problems. The specific case of two data views is considered in this paper. The evaluation with respect to a variety of reference approaches demonstrates the effectiveness of our method in discovering high-quality partitions in a multiview setting. Robustness against unreliable data sources and the ability to automatically determine the number of clusters, are additional advantages evidenced by the results obtained.

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“…Thus, f needs to correctly evaluate the properties that determine a good partition, being responsible for guiding the search process towards high-quality solutions. Many criteria have been proposed so far [21], [22], each presenting a specific formulation to evaluate (either one or a combination of) properties such as intra-cluster homogeneity (compactness), connectedness, and inter-cluster separation. The diversity of existing clustering criteria highlights the lack of consensus on how to assess partition quality, and the fact that it is unlikely that a single solution can simultaneously satisfy all the desirable but (usually) conflicting properties [23].…”

Section: B Multiobjective Clusteringmentioning

confidence: 99%

Decision Making in Evolutionary Multiobjective Clustering: A Machine Learning Challenge

Garza-Fabre¹,

Sánchez-Martínez²,

Aldana-Bobadilla

et al. 2022

IEEE Access

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Evolutionary multiobjective algorithms have become a popular choice to tackle the clustering problem. On the one hand, the simultaneous optimization of complementary clustering criteria offers an increased robustness to changes in data characteristics. On the other hand, the evolutionary search is able to approximate the Pareto optimal front and deliver a set of trade-offs between these criteria in a single algorithm execution. Decision making is the concluding stage of the pipeline, having as its goal the selection of a single, final solution from the set of candidate trade-offs produced. This is a complex task for which a definitive answer does not seem to be available, as the underlying assumptions of existing techniques may not hold for all applications. In this paper, we investigate an alternative approach to address this challenge: posing it as a learning problem. The key idea is to build a model that, given a proper characterization of solutions and their context (defined by the full approximation solution set and the specific clustering task at hand), is able to estimate quality and facilitate the identification of the best choice. To evaluate the suitability of this approach, we conduct a series of experiments over diverse synthetic and real-world datasets, including comparisons against a range of representative decision-making strategies from the literature. Our proposal exhibits greater flexibility in dealing with problems of varying characteristics, consistently outperforming the reference methods considered. This study demonstrates that it is possible to learn from the decisionmaking process in example settings and generalize the acquired knowledge to new scenarios.

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A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering

Cited by 7 publications

References 171 publications

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Decision Making in Evolutionary Multiobjective Clustering: A Machine Learning Challenge

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