Using Decision Trees for Interpretable Supervised Clustering
- URL: http://arxiv.org/abs/2307.08104v1
- Date: Sun, 16 Jul 2023 17:12:45 GMT
- Title: Using Decision Trees for Interpretable Supervised Clustering
- Authors: Natallia Kokash and Leonid Makhnist
- Abstract summary: supervised clustering aims at forming clusters of labelled data with high probability densities.
We are particularly interested in finding clusters of data of a given class and describing the clusters with the set of comprehensive rules.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we address an issue of finding explainable clusters of
class-uniform data in labelled datasets. The issue falls into the domain of
interpretable supervised clustering. Unlike traditional clustering, supervised
clustering aims at forming clusters of labelled data with high probability
densities. We are particularly interested in finding clusters of data of a
given class and describing the clusters with the set of comprehensive rules. We
propose an iterative method to extract high-density clusters with the help of
decisiontree-based classifiers as the most intuitive learning method, and
discuss the method of node selection to maximize quality of identified groups.
Related papers
- Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
We propose a new deep graph clustering method termed Reinforcement Graph Clustering.
In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework.
In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
arXiv Detail & Related papers (2023-08-13T18:12:28Z) - Oracle-guided Contrastive Clustering [28.066047266687058]
Oracle-guided Contrastive Clustering(OCC) is proposed to cluster by interactively making pairwise same-cluster" queries to oracles with distinctive demands.
To the best of our knowledge, it is the first deep framework to perform personalized clustering.
arXiv Detail & Related papers (2022-11-01T12:05:12Z) - Deep Clustering: A Comprehensive Survey [53.387957674512585]
Clustering analysis plays an indispensable role in machine learning and data mining.
Deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks.
Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering.
arXiv Detail & Related papers (2022-10-09T02:31:32Z) - Clustering Optimisation Method for Highly Connected Biological Data [0.0]
We show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data.
The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data.
arXiv Detail & Related papers (2022-08-08T17:33:32Z) - Seeking the Truth Beyond the Data. An Unsupervised Machine Learning
Approach [0.0]
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together.
This article provides a deep description of the most widely used clustering methodologies.
It emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets.
arXiv Detail & Related papers (2022-07-14T14:22:36Z) - DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep
Neural Networks [53.88811980967342]
This paper presents a Deep Clustering via Ensembles (DeepCluE) approach.
It bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks.
Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
arXiv Detail & Related papers (2022-06-01T09:51:38Z) - Self-Evolutionary Clustering [1.662966122370634]
Most existing deep clustering methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping.
A novel modular Self-Evolutionary Clustering (Self-EvoC) framework is constructed, which boosts the clustering performance by classification in a self-supervised manner.
The framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of self-supervised.
arXiv Detail & Related papers (2022-02-21T19:38:18Z) - Self-supervised Contrastive Attributed Graph Clustering [110.52694943592974]
We propose a novel attributed graph clustering network, namely Self-supervised Contrastive Attributed Graph Clustering (SCAGC)
In SCAGC, by leveraging inaccurate clustering labels, a self-supervised contrastive loss, are designed for node representation learning.
For the OOS nodes, SCAGC can directly calculate their clustering labels.
arXiv Detail & Related papers (2021-10-15T03:25:28Z) - You Never Cluster Alone [150.94921340034688]
We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation.
We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one.
By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
arXiv Detail & Related papers (2021-06-03T14:59:59Z) - 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)
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.