Project Background:
The clustering with multiple objectives and constraints encompasses exploring and developing advanced methodologies for unsupervised Learning. Traditional clustering algorithms often optimize a single objective, limiting their adaptability to complex real-world scenarios. The project recognizes the need for a more flexible approach by integrating multiple objectives and constraints into the clustering process. It simultaneously optimizes diverse criteria, such as minimizing intra-cluster variance and maximizing inter-cluster dissimilarity to predefined constraints derived from domain knowledge. The motivation for this research lies in addressing the limitations of conventional clustering methods, ensuring that clustering solutions not only uncover meaningful patterns in the data but also align with practical considerations and domain-specific requirements.
Problem Statement
- The problem in clustering with multiple objectives and constraints revolves around the limitations of traditional clustering algorithms in handling complex real-world scenarios.
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Conventional methods often optimize a single objective, which may not capture the diverse and conflicting goals inherent in many practical applications.
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The challenge is developing robust clustering techniques that simultaneously optimize multiple objectives while adhering to predefined constraints derived from domain knowledge.
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This integration aims to enhance the flexibility, relevance, and interpretability of clustering solutions.
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The project addresses how effectively users balance conflicting objectives during the clustering process.
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Also, how can constraints be incorporated to ensure the practical relevance of clustering outcomes.
Aim and Objectives
- To enhance the effectiveness of unsupervised Learning in real-world scenarios by developing clustering models that optimize multiple objectives to predefined constraints.
- Investigate and integrate diverse clustering objectives to achieve a more comprehensive representation of underlying data patterns.
- Develop algorithms capable of simultaneously optimizing conflicting objectives to provide balanced and nuanced clustering solutions.
- Incorporate domain-specific constraints to ensure the practical relevance and applicability of clustering outcomes.
- Evaluate the performance of the proposed clustering models across various datasets and application domains.
Contributions to Clustering with Multiple Objectives and Constraints
- Introducing a more flexible and adaptable clustering framework by integrating multiple objectives, catering to diverse data patterns and application requirements.
- Developing algorithms capable of simultaneously optimizing conflicting objectives, ensuring a balanced representation of different aspects in the clustering process.
- Incorporating domain-specific constraints to align clustering solutions with practical considerations enhances the relevance of outcomes in real-world scenarios.
- Proposing novel algorithms that advance the state-of-the-art in unsupervised Learning providing more versatile clustering models.
- Contributing to the field through rigorous evaluation methodologies, assessing the performance of clustering models across various datasets and application domains.
Deep Learning Algorithms for Clustering with Multiple Objectives and Constraints
- Autoencoders
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Deep Embedded Clustering (DEC)
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Variational Autoencoders (VAE)
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Self-organizing Maps (SOMs) with Deep Architectures
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Deep Adaptive Resonance Theory (DART)
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Deep K-Means
Datasets for Clustering with Multiple Objectives and Constraints
- MNIST
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CIFAR-10
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Fashion-MNIST
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COIL-20
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UCI Multiple Data Repositories
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ImageNet
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20 Newsgroups
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UC Irvine Machine Learning Repository (UCI-MLR)
Performance Metrics for Clustering with Multiple Objectives and Constraints
- Adjusted Rand Index (ARI)
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Normalized Mutual Information (NMI)
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Fowlkes-Mallows Index (FMI)
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Davies-Bouldin Index
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Silhouette Score
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Homogeneity Score
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Completeness Score
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Inertia Score
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
