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 ...
In this paper, we create a new autoencoder variant to efficiently capture the features of high-dimensional data, and propose an unsupervised deep hashing method for large-scale data retrieval, named as Autoencoder-based Unsupervised Clustering and Hashing (AUCH). By constructing a hashing layer as...
However, most focus on clustering over a low-dimensional feature space. Transform the data into more clustering-friendly representations: A deep version of k-means is based on learning a data representation and applying k-means in the embedded space. How to represent a cluster: a vector VS an...
fromtensorflow.contrib.factorization.python.opsimportclustering_ops importtensorflow as tf unsupervised_model=tf.contrib.learn.KMeansClustering( 10#num of clusters , distance_metric=clustering_ops.SQUARED_EUCLIDEAN_DISTANCE , initial_clusters=tf.contrib.learn.KMeansClustering.RANDOM_INIT ) deftrain_input_f...
One important characteristic of K-means is that it is a hard clustering method, which will associate each point to one and only one cluster. A limitation of this approach is that there is no uncertainty measure probability that tells us how much a data point is associated with a specific ...
Through the analysis of diverse environmental, climatic, and topographical factors, the proposed autoencoder and clustering-based methods provide a holistic solution for identifying areas with peak solar energy potential. The results outline vast swaths of land in different regions in India that can be...
Improvement in clustering accuracy A large number of dropouts in single-cell RNA sequencing data can give a false view of expression levels, which might compromise the integration and interpretation of the data. Such kind of technical and biological noise is bound to trick a clustering algorithm wh...
Graph clustering algorithms with autoencoder structures have recently gained popularity due to their efficient performance and low training cost. However, for existing graph autoencoder clustering algorithms based on GCN or GAT, not only do they lack good generalization ability, but also the number of...
Each autoencoder learns to represent input sequences as lower-dimensional, fixed-size vectors. This can be useful for finding patterns among sequences, clustering sequences, or converting sequences into inputs for other algorithms. Cited in ...
Metapath-based Edge Reconstruction Target Attribute Restoration Positional Feature Prediction Experiments Node classification Node clustering Ablation experiments 来自 美国圣母大学 论文代码github.com/meettyj/HGMAE Introduction Generative self-supervised learning (SSL),尤其是masked autoencoders在图数据挖掘中展...