Project Background:
Graph-based clustering arises from recognizing inherent complexity and interconnectedness in various data domains, where traditional clustering methods may fall short in capturing intricate relationships. Graph-based clustering leverages the power of graph theory to represent data points as nodes and their relationships as edges in a network. This approach is particularly relevant in scenarios where data exhibits non-linear structures and intricate dependencies that are better modeled as graphs. This work aims to apply graph-based clustering techniques to extract meaningful patterns and clusters in datasets, emphasizing their utility in uncovering hidden structures and improving the interpretability of clustering results. By exploiting the topological relationships encoded in graphs, the project seeks to enhance the accuracy and efficiency of clustering algorithms, making them well-suited for domains such as social network analysis, bioinformatics, and image segmentation.
Problem Statement
- The problem in graph-based clustering revolves around the limitations of traditional clustering methods when applied to datasets with complex and non-linear structures.
- Standard clustering algorithms often struggle to capture intricate relationships and dependencies between data points in domains where the underlying patterns are better represented as graphs.
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The challenge lies in efficiently and accurately identifying clusters in datasets that exhibit topological characteristics, such as community structures, dense subgraphs, or interconnected nodes.
- Therefore, the traditional methods may overlook these subtle relationships, leading to suboptimal clustering results.
This work may address the problem by focusing on developing and optimizing graph-based clustering techniques, aiming to enhance the ability to identify meaningful clusters in data with intricate topological dependencies.
Aim and Objectives
- Advanced clustering methodologies enhance the accuracy and efficiency of identifying meaningful clusters within complex and interconnected datasets by exploring and optimizing graph-based clustering techniques.
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Need to develop novel graph-based clustering algorithms.
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Incorporate domain-specific knowledge for improved relevance.
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Handle large-scale and high-dimensional data efficiently.
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Enhance robustness to noise and outliers.
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Evaluate performance on benchmark datasets and real-world applications.
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Improve interpretability of clustering results for complex data structures.
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Investigate scalability for large and evolving datasets.
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Facilitate integration into diverse domains, showcasing versatility and utility.
Contributions to Graph-based Clustering
- The introduction of innovative graph-based clustering algorithms designed to uncover intricate relationships in diverse datasets offers new perspectives on clustering methodologies.
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Developing context-aware clustering models incorporates domain-specific knowledge, enhancing the relevance and interpretability of clustering results in real-world applications.
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Techniques to enhance the robustness of graph-based clustering algorithms, making them resilient to various types of noise and outliers commonly encountered in complex datasets.
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Comprehensive evaluation of algorithmic performance on benchmark datasets provides insights into the strengths and limitations of graph-based clustering compared to existing methods.
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Emphasis on the interpretability of clustering results obtained through graph-based techniques contributes to a deeper understanding of complex data structures and makes the outcomes more actionable.
- Research into the scalability, ensuring their applicability to large and evolving datasets and addressing challenges associated with increasing data volume.
- Facilitation of cross-domain integration methodologies showcasing their versatility and utility in diverse applications.
- Contributions to addressing the scalability challenge in graph-based clustering provide efficient solutions for handling large-scale and high-dimensional data while maintaining clustering accuracy.
Deep Learning Algorithms for Graph-based Clustering
- Graph Convolutional Networks (GCNs)
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GraphSAGE (Graph Sample and Aggregated)
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Graph Attention Networks (GAT)
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DeepWalk
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Node2Vec
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Graph Isomorphism Networks (GIN)
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Graph Autoencoders
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Graph Neural Networks (GNNs)
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Graph-Based Variational Autoencoders
Datasets for Graph-based Clustering
- Cora
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Citeseer
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PubMed
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Reddit
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Enron
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Amazon Product Co-purchasing
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Facebook Social Circles
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Karate Club
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Protein-Protein Interaction Networks
Performance Metrics for Graph-based Clustering
- Modularity
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Normalized Mutual Information (NMI)
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F1 Score
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Precision
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Recall
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Silhouette Score
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Davies-Bouldin Index
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Rand Index
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Jaccard Index
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
