Random Models for Fuzzy Clustering Similarity Measures

• URL: http://arxiv.org/abs/2312.10270v1
• Date: Sat, 16 Dec 2023 00:07:04 GMT
• Title: Random Models for Fuzzy Clustering Similarity Measures
• Authors: Ryan DeWolfe and Jeffery L. Andrews
• Abstract summary: The Adjusted Rand Index (ARI) is a widely used method for comparing hard clusterings. We propose a single framework for computing the ARI with three random models that are intuitive and explainable for both hard and fuzzy clusterings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Adjusted Rand Index (ARI) is a widely used method for comparing hard clusterings, but requires a choice of random model that is often left implicit. Several recent works have extended the Rand Index to fuzzy clusterings, but the assumptions of the most common random model is difficult to justify in fuzzy settings. We propose a single framework for computing the ARI with three random models that are intuitive and explainable for both hard and fuzzy clusterings, along with the benefit of lower computational complexity. The theory and assumptions of the proposed models are contrasted with the existing permutation model. Computations on synthetic and benchmark data show that each model has distinct behaviour, meaning that accurate model selection is important for the reliability of results.

Related papers

• Predictive Modeling in the Reservoir Kernel Motif Space [0.9217021281095907]
  This work proposes a time series prediction method based on the kernel view of linear reservoirs. We provide a geometric interpretation of our approach shedding light on how our approach is related to the core reservoir models. Empirical experiments then compare predictive performances of our suggested model with those of recent state-of-art transformer based models.
  arXiv  Detail & Related papers  (2024-05-11T16:12:25Z)
• Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data [0.8499685241219366]
  A class of eight parsimonious mixture models based on the mixtures of factor analyzers model are introduced. The proposed models are explored in the context of clustering discrete data arising from RNA sequencing studies.
  arXiv  Detail & Related papers  (2023-11-13T21:23:15Z)
• Why the Rich Get Richer? On the Balancedness of Random Partition Models [1.776746672434207]
  We study the balancedness of exchangeable random partition models. We demonstrate that the "rich-get-richer" characteristic of many existing popular random partition models is an inevitable consequence of two common assumptions. We also introduce the "rich-get-poorer" random partition models and illustrate their application to entity resolution tasks.
  arXiv  Detail & Related papers  (2022-01-30T01:19:41Z)
• Universal and data-adaptive algorithms for model selection in linear contextual bandits [52.47796554359261]
  We consider the simplest non-trivial instance of model-selection: distinguishing a simple multi-armed bandit problem from a linear contextual bandit problem. We introduce new algorithms that explore in a data-adaptive manner and provide guarantees of the form $mathcalO(dalpha T1- alpha)$. Our approach extends to model selection among nested linear contextual bandits under some additional assumptions.
  arXiv  Detail & Related papers  (2021-11-08T18:05:35Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)
• Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach [6.509758931804479]
  The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification. Results show that mixture models improve the overall performance of latent class choice models.
  arXiv  Detail & Related papers  (2020-07-06T13:19:26Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study [0.0]
  We use pattern similarity-based methods for forecasting monthly electricity demand expressing annual seasonality. An integral part of the models is the time series representation using patterns of time series sequences. We consider four such models: nearest neighbor model, fuzzy neighborhood model, kernel regression model and general regression neural network.
  arXiv  Detail & Related papers  (2020-03-03T12:14:36Z)
• Learning Gaussian Graphical Models via Multiplicative Weights [54.252053139374205]
  We adapt an algorithm of Klivans and Meka based on the method of multiplicative weight updates. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature. It has a low runtime $O(mp2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
  arXiv  Detail & Related papers  (2020-02-20T10:50:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.