A new approach for evaluating internal cluster validation indices

• URL: http://arxiv.org/abs/2308.03894v1
• Date: Wed, 2 Aug 2023 06:55:33 GMT
• Title: A new approach for evaluating internal cluster validation indices
• Authors: Zolt\'an Botta-Duk\'at
• Abstract summary: Cluster validation is needed to select the best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. Evaluation approaches differ in how they use the information on the ground-truth classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A vast number of different methods are available for unsupervised classification. Since no algorithm and parameter setting performs best in all types of data, there is a need for cluster validation to select the actually best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. These internal validation indices can be evaluated by applying them to classifications of datasets with a known cluster structure. Evaluation approaches differ in how they use the information on the ground-truth classification. This paper reviews these approaches, considering their advantages and disadvantages, and then suggests a new approach.

Related papers

• From A-to-Z Review of Clustering Validation Indices [4.08908337437878]
  We review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms. We suggest a classification framework for examining the functionality of both internal and external clustering validation measures.
  arXiv  Detail & Related papers  (2024-07-18T13:52:02Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments [54.172993875654015]
  The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments. One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
  arXiv  Detail & Related papers  (2022-09-22T09:04:10Z)
• The Three Ensemble Clustering (3EC) Algorithm for Pattern Discovery in Unsupervised Learning [1.0465883970481493]
  The 'Three Ensemble Clustering 3EC' algorithm classifies unlabeled data into quality clusters as a part of unsupervised learning. Each partitioned cluster is considered to be a new data set and is a candidate to explore the most optimal algorithm. The users can experiment with different sets of stopping criteria and choose the most'sensible group' of quality clusters.
  arXiv  Detail & Related papers  (2021-07-08T10:15:18Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Predictive K-means with local models [0.028675177318965035]
  Predictive clustering seeks to obtain the best of the two worlds. We present two new algorithms using this technique and show on a variety of data sets that they are competitive for prediction performance.
  arXiv  Detail & Related papers  (2020-12-16T10:49:36Z)
• 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)
• Structured Graph Learning for Clustering and Semi-supervised Classification [74.35376212789132]
  We propose a graph learning framework to preserve both the local and global structure of data. Our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Our model is equivalent to a combination of kernel k-means and k-means methods under certain condition.
  arXiv  Detail & Related papers  (2020-08-31T08:41:20Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• A New Validity Index for Fuzzy-Possibilistic C-Means Clustering [6.174448419090291]
  Fuzzy-Possibilistic (FP) index works well in the presence of clusters that vary in shape and density. FPCM requires a priori selection of the degree of fuzziness and the degree of typicality.
  arXiv  Detail & Related papers  (2020-05-19T01:48:13Z)
• 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.