Automatic selection of clustering algorithms using supervised graph embedding

• URL: http://arxiv.org/abs/2011.08225v3
• Date: Mon, 13 Sep 2021 07:26:54 GMT
• Title: Automatic selection of clustering algorithms using supervised graph embedding
• Authors: Noy Cohen-Shapira and Lior Rokach
• Abstract summary: MARCO-GE is a novel meta-learning approach for the automated recommendation of clustering algorithms. It trains a ranking meta-model capable of accurately recommending top-performing algorithms for a new dataset and clustering evaluation measure.
• Score: 14.853602181549967
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led to increased interest in automated ML solutions that reduce the need for human intervention. One of the main challenges in applying ML to previously unseen problems is algorithm selection - the identification of high-performing algorithm(s) for a given dataset, task, and evaluation measure. This study addresses the algorithm selection challenge for data clustering, a fundamental task in data mining that is aimed at grouping similar objects. We present MARCO-GE, a novel meta-learning approach for the automated recommendation of clustering algorithms. MARCO-GE first transforms datasets into graphs and then utilizes a graph convolutional neural network technique to extract their latent representation. Using the embedding representations obtained, MARCO-GE trains a ranking meta-model capable of accurately recommending top-performing algorithms for a new dataset and clustering evaluation measure. Extensive evaluation on 210 datasets, 13 clustering algorithms, and 10 clustering measures demonstrates the effectiveness of our approach and its superiority in terms of predictive and generalization performance over state-of-the-art clustering meta-learning approaches.

Related papers

• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization [4.173197621837912]
  We conduct an overview of the key contributions to algorithm selection in the field of single-objective continuous black-box optimization. We study machine learning models for automated algorithm selection, configuration, and performance prediction.
  arXiv  Detail & Related papers  (2024-06-08T11:11:14Z)
• A Weighted K-Center Algorithm for Data Subset Selection [70.49696246526199]
  Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data. We develop a novel factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions.
  arXiv  Detail & Related papers  (2023-12-17T04:41:07Z)
• Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification [17.284276598514502]
  We propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment. To prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm.
  arXiv  Detail & Related papers  (2023-05-22T04:26:18Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• Meta Clustering Learning for Large-scale Unsupervised Person Re-identification [124.54749810371986]
  We propose a "small data for big task" paradigm dubbed Meta Clustering Learning (MCL) MCL only pseudo-labels a subset of the entire unlabeled data via clustering to save computing for the first-phase training. Our method significantly saves computational cost while achieving a comparable or even better performance compared to prior works.
  arXiv  Detail & Related papers  (2021-11-19T04:10:18Z)
• Riemannian classification of EEG signals with missing values [67.90148548467762]
  This paper proposes two strategies to handle missing data for the classification of electroencephalograms. The first approach estimates the covariance from imputed data with the $k$-nearest neighbors algorithm; the second relies on the observed data by leveraging the observed-data likelihood within an expectation-maximization algorithm. As results show, the proposed strategies perform better than the classification based on observed data and allow to keep a high accuracy even when the missing data ratio increases.
  arXiv  Detail & Related papers  (2021-10-19T14:24:50Z)
• Learning the Precise Feature for Cluster Assignment [39.320210567860485]
  We propose a framework which integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-11T04:08:54Z)
• DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning [0.0]
  We propose DAC, Deep Autoencoder-based Clustering, a data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the KMeans clustering algorithm on a variety of datasets.
  arXiv  Detail & Related papers  (2021-02-15T11:31:00Z)
• 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)

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.