RECORD
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning
Machine Learning · Submitted 15 Feb 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Clustering performs an essential role in many real world applications, such as market research, pattern recognition, data analysis, and image processing. However, due to the high dimensionality of the input feature values, the data being fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results. While traditional dimension reduction and feature selection algorithms could be used to address this problem, the simple heuristic rules used in those algorithms are based on some particular assumptions. When those assumptions does not hold, these algorithms then might not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Clustering performs an essential role in many real world applications, such as market research, pattern recognition, data analysis, and image processing. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | However, due to the high dimensionality of the input feature values, the data being fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results. | background 0.96 | 0.08 | 0.02 | hedged possibility 0.96 |
| s2 | While traditional dimension reduction and feature selection algorithms could be used to address this problem, the simple heuristic rules used in those algorithms are based on some particular assumptions. | background 0.76 | 0.05 | 0.02 | asserted 0.78 |
| s3 | When those assumptions does not hold, these algorithms then might not work. | background 0.62 | 0.04 | 0.02 | speculative 0.75 |
| s4 | In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks. | contribution 0.97 | 0.82 | 0.18 | asserted 0.99 |
| s5 | Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets. | result 1.00 | 0.96 | 0.83 | hedged possibility 0.72 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Clustering performs an essential role in many real world applications, such as market research, pattern recognition, data analysis, and image processing. However, due to the high dimensionality of the input feature values, the data being fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results. While traditional dimension reduction and feature selection algorithms could be used to address this problem, the simple heuristic rules used in those algorithms are based on some particular assumptions. When those assumptions does not hold, these algorithms then might not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets.
Human review
How the literature uses it
1 arXiv paper cite this in 1 sentence
What it cites
Uses data or values from 1 paper
-
To test the robustness of our approach against different data types, we apply our method to two other datasets: Fashion-MNIST [ref] , and Human Activities and Postural Transitions Data Set (HAPT) [ref] .
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms · Results on other datasets · 1.00
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.