Applying Semi-Automated Hyperparameter Tuning for Clustering Algorithms

• URL: http://arxiv.org/abs/2108.11053v1
• Date: Wed, 25 Aug 2021 05:48:06 GMT
• Title: Applying Semi-Automated Hyperparameter Tuning for Clustering Algorithms
• Authors: Elizabeth Ditton, Anne Swinbourne, Trina Myers, Mitchell Scovell
• Abstract summary: This study proposes a framework for semi-automated hyperparameter tuning of clustering problems. It uses a grid search to develop a series of graphs and easy to interpret metrics that can then be used for more efficient domain-specific evaluation. Preliminary results show that internal metrics are unable to capture the semantic quality of the clusters developed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: When approaching a clustering problem, choosing the right clustering algorithm and parameters is essential, as each clustering algorithm is proficient at finding clusters of a particular nature. Due to the unsupervised nature of clustering algorithms, there are no ground truth values available for empirical evaluation, which makes automation of the parameter selection process through hyperparameter tuning difficult. Previous approaches to hyperparameter tuning for clustering algorithms have relied on internal metrics, which are often biased towards certain algorithms, or having some ground truth labels available, moving the problem into the semi-supervised space. This preliminary study proposes a framework for semi-automated hyperparameter tuning of clustering problems, using a grid search to develop a series of graphs and easy to interpret metrics that can then be used for more efficient domain-specific evaluation. Preliminary results show that internal metrics are unable to capture the semantic quality of the clusters developed and approaches driven by internal metrics would come to different conclusions than those driven by manual evaluation.

Related papers

• Exploratory Landscape Analysis for Mixed-Variable Problems [0.7252027234425334]
  We provide the means to compute exploratory landscape features for mixed-variable problems where the decision space is a mixture of continuous, binary, integer, and categorical variables. To further highlight their merit for practical applications, we design and conduct an automated algorithm selection study. Our trained algorithm selector is able to close the gap between the single best and the virtual best solver by 57.5% over all benchmark problems.
  arXiv  Detail & Related papers  (2024-02-26T10:19:23Z)
• Robust and Automatic Data Clustering: Dirichlet Process meets Median-of-Means [18.3248037914529]
  We present an efficient and automatic clustering technique by integrating the principles of model-based and centroid-based methodologies. Statistical guarantees on the upper bound of clustering error suggest the advantages of our proposed method over existing state-of-the-art clustering algorithms.
  arXiv  Detail & Related papers  (2023-11-26T19:01:15Z)
• Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [79.46465138631592]
  We devise an efficient algorithm that recovers clusters using the observed labels. We present Instance-Adaptive Clustering (IAC), the first algorithm whose performance matches these lower bounds both in expectation and with high probability.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning [20.995946115633963]
  We propose a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications.
  arXiv  Detail & Related papers  (2023-05-01T01:04:07Z)
• Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation [65.268245109828]
  Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments.
  arXiv  Detail & Related papers  (2023-03-29T08:23:26Z)
• Gradient Based Clustering [72.15857783681658]
  We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions.
  arXiv  Detail & Related papers  (2022-02-01T19:31:15Z)
• Personalized Federated Learning via Convex Clustering [72.15857783681658]
  We propose a family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized.
  arXiv  Detail & Related papers  (2022-02-01T19:25:31Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)
• Stable and consistent density-based clustering via multiparameter persistence [77.34726150561087]
  We consider the degree-Rips construction from topological data analysis. We analyze its stability to perturbations of the input data using the correspondence-interleaving distance. We integrate these methods into a pipeline for density-based clustering, which we call Persistable.
  arXiv  Detail & Related papers  (2020-05-18T19:45:04Z)
• A semi-supervised sparse K-Means algorithm [3.04585143845864]
  An unsupervised sparse clustering method can be employed in order to detect the subgroup of features necessary for clustering. A semi-supervised method can use the labelled data to create constraints and enhance the clustering solution. We show that the algorithm maintains the high performance of other semi-supervised algorithms and in addition preserves the ability to identify informative from uninformative features.
  arXiv  Detail & Related papers  (2020-03-16T02:05:23Z)
• Simple and Scalable Sparse k-means Clustering via Feature Ranking [14.839931533868176]
  We propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms. Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings.
  arXiv  Detail & Related papers  (2020-02-20T02:41:02Z)

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.