Project Background:
Adversarial Clustering emerges from the need to address the limitations of traditional clustering methods in handling complex and high-dimensional data distributions. Traditional clustering algorithms often rely on assumptions of data separability and uniformity may not hold in real-world scenarios with intricate data distributions or overlapping clusters. Adversarial Clustering aims to overcome these limitations by leveraging adversarial learning techniques to enhance the robustness and flexibility of clustering algorithms.
By incorporating adversarial training mechanisms, the project seeks to develop clustering models that can effectively capture the underlying structure of complex data distributions, even in the presence of noise, outliers, or overlapping clusters. The project also explores theoretical foundations, algorithmic frameworks, and practical applications of adversarial clustering in various domains. Overall, it sets the stage for developing innovative approaches to clustering that can improve the accuracy, robustness, and scalability of clustering models in complex data environments.
Problem Statement
- Traditional clustering methods struggle to handle complex and high-dimensional data distributions effectively, leading to suboptimal cluster assignments.
- In real-world scenarios, clusters may overlap or exhibit non-linear separability, posing challenges for traditional clustering algorithms to delineate cluster boundaries accurately.
- Traditional clustering algorithms are sensitive to noise and outliers in the data, which can adversely affect the quality of cluster assignments and degrade clustering performance.
- Existing clustering methods may lack robustness in the face of adversarial attacks or perturbations, making them susceptible to manipulation and exploitation by adversaries.
- Scalability becomes a concern when dealing with large-scale or high-dimensional datasets, as traditional clustering algorithms may struggle to process and analyze such data efficiently.
- Complex clustering algorithms may lack interpretability and transparency, making understanding and interpreting cluster assignments difficult, particularly in real-world applications where explainability is crucial.
Aim and Objectives
- To enhance the robustness and accuracy of clustering algorithms in complex data environments through adversarial learning techniques.
- Develop adversarial clustering algorithms capable of handling complex and high-dimensional data distributions.
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Improve the robustness of clustering models to noise, outliers, and adversarial attacks.
- Address the challenges of cluster overlapping and non-linear separability in real-world data scenarios.
- Explore methods for enhancing the scalability and efficiency of adversarial clustering algorithms.
- Evaluate the effectiveness of adversarial clustering techniques on various datasets and real-world applications.
Contributions to Adversarial Clustering
- Development of robust clustering algorithms capable of handling complex and high-dimensional data distributions.
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Improvement of clustering model robustness to noise, outliers, and adversarial attacks.
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Addressing challenges related to cluster overlapping and non-linear separability in real-world data scenarios.
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Exploration of methods for enhancing the scalability and efficiency of adversarial clustering algorithms.
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Evaluation of the effectiveness of adversarial clustering techniques on diverse datasets and real-world applications.
Deep Learning Algorithms for Adversarial Clustering
- Adversarial Autoencoder Clustering (AAC)
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Adversarial Deep Embedding Clustering (ADEC)
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Adversarial Variational Clustering (AVC)
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Adversarial Graph Convolutional Clustering (AGCC)
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Adversarial Hierarchical Clustering (AHC)
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Adversarial Self-Organizing Maps (ASOM)
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Adversarial Density-Based Clustering (ADBC)
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Adversarial Spectral Clustering (ASC)
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Adversarial Mean Shift Clustering (AMSC)
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Adversarial K-Means Clustering (AKMC)
Datasets for Adversarial Clustering
- MNIST
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CIFAR-10
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ImageNet
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Fashion-MNIST
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COCO (Common Objects in Context)
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LSUN (Large-Scale Scene Understanding)
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LFW (Labeled Faces in the Wild)
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CelebA
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SVHN (Street View House Numbers)
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USPS (United States Postal Service Database)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
