Project Background:
The projects background in active clustering is rooted in the evolving landscape of machine learning applications, where traditional clustering methods face challenges in efficiently handling large, high-dimensional datasets. Active clustering emerges as a strategic solution to enhance the accuracy and interpretability of clustering models by actively selecting and labeling the most informative data points. The motivation stems from scenarios where labeling every data point is resource-intensive or impractical. The project aims to bridge the gap between the scalability of clustering algorithms and the need for interpretable and accurate results in various domains. By iteratively querying instances for additional information, active clustering optimizes the labeling process and refines the cluster assignments dynamically. This work highlights the commitment of the project to advancing clustering methodologies and addressing the challenges posed by complex and real-world datasets. Ultimately, active clustering seeks to contribute to developing more robust and efficient clustering models tailored for contemporary machine learning applications.
Problem Statement
- The problem in active clustering revolves around the challenges traditional clustering methods face when applied to large, dynamic datasets.
- Conventional clustering approaches often lack the scalability and adaptability required for real-world scenarios when dealing with high-dimensional data.
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The primary issue lies in the passive analysis of the entire dataset, which becomes impractical and resource-intensive in applications where obtaining labels for every data point is challenging.
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The problem centers on the need for more efficient and effective clustering techniques to actively select and label informative instances, aiming to improve the accuracy and interpretability of the clustering model.
- Active clustering addresses this challenge by iteratively refining clusters through selective querying, ensuring a more focused and adaptive approach to clustering in the face of evolving data distributions.
Aim and Objectives
- The project in active clustering aims to enhance the efficiency and effectiveness of clustering algorithms by incorporating active learning principles, specifically targeting scenarios where obtaining labels for every data point is resource-intensive or impractical.
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Develop and implement adaptive querying strategies for active clustering.
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Improve the scalability of clustering algorithms in the face of large and dynamic datasets.
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Enhance the interpretability of clustering models by iteratively refining clusters based on selectively labeled instances.
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Investigate integrating advanced machine learning techniques such as deep Learning to boost the representation learning capabilities of active clustering models.
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Address robustness, security, and adaptability challenges in active clustering, ensuring reliable performance in diverse application domains.
Contributions to Active Clustering
- Develop novel querying strategies that adapt dynamically to the characteristics of the dataset, improving the efficiency of active clustering by selecting the most informative instances for labeling.
- Introduce methods to improve the interpretability of clustering models by actively refining clusters based on selectively labeled instances to ensure the resulting clusters align more closely with underlying patterns in the data.
- Explore the integration to bolster the representation learning capabilities of active clustering models, enabling to capture of more intricate patterns.
- Address challenges related to robustness and security in active clustering to enhance the reliability and trustworthiness of the clustering results in the face of potential adversarial scenarios.
- Apply active clustering methodologies to diverse application domains, showcasing the versatility and utility of the developed techniques.
- Investigate and contribute to semi-supervised active clustering approaches leveraging labeled and unlabeled data to handle scenarios with limited labeled instances more effectively.
- Contribute methods for active clustering can dynamically adapt to evolving data distributions, ensuring that the algorithm remains effective and relevant in dynamic environments.
- Explore multi-objective optimization approaches in active clustering, considering objectives such as diversity, scalability, and interpretability to provide a more comprehensive and adaptable clustering solution.
Deep Learning Algorithms for Active Clustering
- Autoencoders
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Deep Embedding Clustering (DEC)
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Self-Organizing Maps (SOM) with Deep Architectures
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Deep Embedded Clustering (DEC)
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Deep k-Means
Datasets for Active Clustering
- MNIST
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CIFAR-10
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ImageNet
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UCI ML Repository
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Reuters-21578 Text Categorization Dataset
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Genomic Data for Bioinformatics Clustering
Performance Metrics for Active Clustering
- Precision
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Recall
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F1 Score for Clusters
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Silhouette Score
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Davies-Bouldin Index
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Adjusted Rand Index (ARI)
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Fowlkes-Mallows Index
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Normalized Mutual Information (NMI)
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Jaccard Index
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Huberts Gamma Index
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Intra-Cluster Distance and Inter-Cluster Distance
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
