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This paper presents a new algorithm, Reinforced and Informed Network-based Clustering (RINC), for finding unknown groups of similar data objects in sparse and largely non-overlapping feature space where a network structure among features can be observed. Sparse and non-overlapping unlabeled data become increasingly common and available especially in text mining and biomedical data mining. RINC inserts a domain informed model into a modelless neural network. In particular, our approach integrates physically meaningful feature dependencies into the neural network architecture and soft computational constraint. Our learning algorithm efficiently clusters sparse data through integrated smoothing and sparse auto-encoder learning. The informed design requires fewer samples for training and at least part of the model becomes explainable. The architecture of the reinforced network layers smooths sparse data over the network dependency in the feature space. Most importantly, through back-propagation, the weights of the reinforced smoothing layers are simultaneously constrained by the remaining sparse auto-encoder layers that set the target values to be equal to the raw inputs. Empirical results demonstrate that RINC achieves improved accuracy and renders physically meaningful clustering results.
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Robust Embedded Deep K-means Clustering
Deep neural network clustering is superior to the conventional clustering methods due to deep feature extraction and nonlinear dimensionality reduction. Nevertheless, deep neural network leads to a rough representation regarding the inherent relationship of the data points. Therefore, it is still difficult for deep neural network to exploit the effective structure for direct clustering. To address this issue,we propose a robust embedded deep K-means clustering (REDKC) method. The proposed RED-KC approach utilizes the δ-norm metric to constrain the feature mapping process of the auto-encoder network, so that data are mapped to a latent feature space, which is more conducive to the robust clustering. Compared to the existing auto-encoder networks with the fixed prior, the proposed RED-KC is adaptive during the process of feature mapping. More importantly, the proposed RED-KC embeds the clustering process with the autoencoder network, such that deep feature extraction and clustering can be performed simultaneously. Accordingly, a direct and efficient clustering could be obtained within only one step to avoid the inconvenience of multiple separate stages, namely, losing pivotal information and correlation. Consequently, extensive experiments are provided to validate the effectiveness of the proposed approach.
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- PAR ID:
- 10159291
- Date Published:
- Journal Name:
- CIKM
- Page Range / eLocation ID:
- 1181 to 1190
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3357384.3357985
