Learning Data Representation for Clustering

In conjunction with  The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023) , Osaka, Japan, from May 25 to May 28, 2023

Call for papers

This workshop aims at discovering the recent advanced on data representation for clustering under different approaches. Thereby, the LDRC workshop is an opportunity to:

• present the recent advances in data representation based clustering algorithms,

• outline potential applications that could inspire new data representation approaches for clustering,

• explore benchmark data to better evaluate and study data representation based clustering models

This workshop intends to promote research at the intersection of data representation learning and clustering, and its application to real-life data mining challenges. The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers on unsupervised data representation learning for clustering and related work. For reference, here is a non-exclusive list of topics of interest:

• Technical areas - Unsupervised data representation learning, Deep network embedding, Attributed graph embedding, Manifold learning, Dimensionality reduction Spectral data embedding, Factorization, Tensor clustering, Co-clustering , Latent Block Models, Graph Laplacian Mixture Models, Subspace clustering, Visualization

To attract researchers from various communities, this workshop will encourage submissions on applications, especially those that motivate the development of powerful representation-learning models, such as

• Application areas - Bioinformatics, Medicine, Recommendation Systems, Computer Vision, Text mining, Natural Language Processing