/Awesome-Semi-supervised-Clustering

Awesome-Semi-supervised-Clustering

What is semi-supervised cluster learning？

Semi-supervised cluster learning is a new machine learning method that combines semi-supervised learning and cluster learning. In traditional unsupervised clustering learning, the data is usually unlabeled data, but in fact, not all data are unlabeled, but traditional methods cannot use these labeled data to improve the clustering effect. The data objects of semi-supervised clustering learning include both unlabeled data and labeled data. Using these data, semi-supervised clustering methods can effectively improve clustering performance.

Common semi-supervised clustering（SCC） methods

  • partition-based SSC
  • hierarchical-based SSC
  • density-based SSC
  • graph-based SSC
  • neural network-based SSC
  • Nonnegative Matrix Factorization-based SSC
  • random subspace technique-based SSC

Paper

partition-based SSC

Year Title
2012 Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems
2002 Semi-supervised clustering by seeding
2005 Clustering With Constraints: Feasibility Issues and the k-Means Algorithm
2001 [ Constrained k-means clustering with background knowledge](https://web.cse.msu.edu/~cse802/notes/ConstrainedKmeans.pdf)
2021 SMKFC-ER: Semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy
2009 Semi-supervised fuzzy clustering: A kernel-based approach
2012 Semi-supervised fuzzy clustering with metric learning and entropy regularization
2020 Pairwise-Constraints Based Semi-Supervised Fuzzy Clustering with Entropy Regularization
2014 Clustering of Biomedical documents using semi supervised clustering method

hierarchical-based SSC

Year Paper
2021 Revisiting agglomerative clustering
2011 Semi-supervised hierarchical clustering
2010 Semi-supervised agglomerative hierarchical clustering algorithms with pairwise constraints
2012 Hcac: Semi-supervised hierarchical clustering using confidence-based active learning

density-based SSC

2011 Effective semi-supervised document clustering via active learning with instance-level constraints
2016 An adaptive semi-supervised clustering approach via multiple density-based information
2018 Semi-supervised DenPeak Clustering with Pairwise Constraints
2018 An efficient semi-supervised graph based clustering

graph-based SSC

2011 Spectral clustering: A semi-supervised approach
2015 Large-scale spectral clustering based on pairwise constraints
2016 Affinity and penalty jointly constrained spectral clustering with all-compatibility, flexibility, and robustness
2019 Auto-weighted Multi-view learning for Semi-Supervised graph clustering

neural network-based SSC

2020 AutoEmbedder: A semi-supervised DNN embedding system for clustering
2020 A classification-based approach to semi-supervised clustering with pairwise constraints
2020 AutoEmbedder: A semi-supervised DNN embedding system for clustering

Nonnegative Matrix Factorization-based SSC

2017 Graph-based discriminative nonnegative matrix factorization with label information

random subspace technique-based SSC

1998 The random subspace method for constructing decision forests

Dataset

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