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Found 28 papers, 8 papers with code
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels.
DISCO: Internal Evaluation of Density-Based Clustering
In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels.
I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies
Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen.
Motif Discovery Framework for Psychiatric EEG Data Classification
In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants.
Plasticity Loss in Deep Reinforcement Learning: A Survey
With this survey, we aim to provide an overview of the emerging research on plasticity loss for academics and practitioners of deep reinforcement learning.
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains.
Ranked #4 on
Unsupervised Image Classification
on CIFAR-20
SHADE: Deep Density-based Clustering
Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder.
Temporal Subspace Clustering for Molecular Dynamics Data
We introduce MOSCITO (MOlecular Dynamics Subspace Clustering with Temporal Observance), a subspace clustering for molecular dynamics data.
Text-Guided Alternative Image Clustering
Traditional image clustering techniques only find a single grouping within visual data.
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
The findings further underscore the effectiveness of LLMs in enhancing anomaly detection in financial journal entries, particularly by tackling feature sparsity.
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method.
Text-Guided Image Clustering
We, therefore, propose Text-Guided Image Clustering, i. e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text.
Automatic Parameter Selection for Non-Redundant Clustering
In this paper, we propose a framework that utilizes the Minimum Description Length Principle (MDL) to detect the number of subspaces and clusters per subspace automatically.
Extension of the Dip-test Repertoire -- Efficient and Differentiable p-value Calculation for Clustering
This accelerates computation and provides an approximation of the Dip- to Dip-p-value transformation for every single sample size.
Interpretable Subgraph Feature Extraction for Hyperlink Prediction
In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features.
Spectral Clustering of Attributed Multi-relational Graphs
Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes.
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL).
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices.
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree
Both clustering and outlier detection play an important role for meteorological measurements.
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN.
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms.
Deep Clustering With Consensus Representations
Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result.
Interpretable Gait Recognition by Granger Causality
We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait.
Causal Discovery in Hawkes Processes by Minimum Description Length
The synthetic experiments demonstrate superiority of our method incausal graph discovery compared to the baseline methods with respect to the size of the data.
GPU backed Data Mining on Android Devices
We have created a wrapper library that allows to use OpenCL on Android devices.
Network Embedding via Deep Prediction Model
This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models.
Massively Parallel Graph Drawing and Representation Learning
To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways.
Incorporating User's Preference into Attributed Graph Clustering
In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs).
