Data Clustering

Data Clustering

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  Faculty of ComputerEngineering Seminar for Master degree in The Major of Artificial Intelligence And Robotic Title DATA CLUSTERING ‫ها‬ ‫داده‬ ‫بندی‬ ‫خوشه‬ Supervisor Associate professor. Askar Poer Advisor Prof. ………… Researcher Mohammed Ayoub Mamaseeni
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  Outline  Introduction  Whatis Data Clustering?  Types of Clustering Algorithms  K-Means Clustering  Hierarchical Clustering  DBSCAN Clustering  Choosing the Right Clustering Algorithm  Evaluating Clustering Performance  Applications of Data Clustering  Conclusion and Key Take aways 2
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  Introduction to Data Clustering Dataclustering is a powerful technique in machine learning and data analysis that groups similar data points together, revealing underlying patterns and structures within complex datasets. This provides valuable insights for a wide range of applications, from customer segmentation to image recognition. 3
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  What is DataClustering? Data clustering is the process of grouping similar data points together into distinct clusters or groups. The goal is to identify natural patterns and structures within complex datasets, enabling deeper insights and better decision-making. By organizing data into meaningful clusters, analysts can uncover hidden relationships and trends that may not be immediately apparent. 4
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  Types of Clustering Algorithms 1.Partitioning Algorithms: These divide data into k distinct clusters, such as K-Means, which assigns each data point to the nearest cluster center. 2. Hierarchical Algorithms: These build a hierarchy of clusters, allowing analysis at different levels of granularity, like Agglomerative and Divisive clustering. 3. Density-Based Algorithms: These identify clusters based on the density of data points, like DBSCAN, which finds high- density regions separated by low-density areas. 5
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  K-Means Clustering K-Means isa popular partitioning clustering algorithm that groups data points into k distinct clusters based on their similarity. It works by iteratively assigning each data point to the nearest cluster centroid and then recalculating the centroids until convergence. The key advantages of K-Means are its simplicity, scalability, and the ability to handle large datasets effectively. It is widely used in customer segmentation, image segmentation, and anomaly detection applications. 6
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  Hierarchical Clustering Hierarchical clusteringis a powerful technique that builds a hierarchy of clusters, allowing analysis at different levels of granularity. It can identify complex, nested structures within data by iteratively merging or splitting clusters based on their proximity. This approach is particularly useful when the number of clusters is unknown or the data exhibits a clear hierarchical relationship. Hierarchical methods include Agglomerative and Divisive clustering, each with its own strengths and applications. 7
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  DBSCAN Clustering Density-Based Clustering DBSCAN isa density-based clustering algorithm that groups together data points that are close to each other based on density, identifying clusters of arbitrary shape and size. Handling Outliers One of the key advantages of DBSCAN is its ability to identify and handle outliers, which are data points that do not belong to any well- defined cluster. Parameters and Considerations The performance of DBSCAN depends on the selection of its two key parameters, epsilon (eps) and the minimum number of points (minPoints), which determine the density threshold for cluster formation. 8
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  Choosing the RightClustering Algorithm Data Characteristics Consider the size, dimensionality, and structure of your dataset. Different algorithms excel with specific data types and properties. Cluster Shapes K-Means works best for spherical clusters, while DBSCAN can handle arbitrary shapes. Hierarchical methods suit nested structures. Noise Handling DBSCAN can identify and isolate outliers, while K- Means is more sensitive to noise. Hierarchical methods have varied noise tolerance. Computational Efficiency K-Means is highly scalable, while DBSCAN and hierarchical methods can be more computationally intensive for large datasets. 9
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  Evaluating Clustering Performance Assessingthe quality and effectiveness of clustering models is crucial to ensure they deliver meaningful insights. Several evaluation metrics can be used to measure clustering performance, such as intra-cluster distance, inter-cluster distance, and silhouette score. The chart presents the performance of a clustering model based on three key evaluation metrics. The low intra-cluster distance and high inter-cluster distance indicate that the clusters are well- separated and compact. The silhouette score, which measures how well each data point fits its assigned cluster, further validates the clustering quality. 10
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  Applications of DataClustering Customer Segmentation Cluster customers based on their behaviors, preferences, and demographics to personalize marketing and improve user experiences. Biomedical Research Identify subgroups of patients with similar genetic profiles or disease characteristics to enable precision medicine. Image Segmentation Partition images into meaningful regions or objects, enabling applications like object detection and recognition. Network Analysis Cluster nodes in a network to uncover communities, detect anomalies, and understand complex relationships. 11
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  Related Studies 1- Two-prongedfeature reduction in spectral clustering with optimized https://scholar.google.com/citations?view_op=view_citation&hl=en&user=qNQSCOoAAAAJ&pagesize=80&citft=3&email_for_op= mahamad97ayoub%40gmail.com&authuser=1&citation_for_view=qNQSCOoAAAAJ:EUQCXRtRnyEC The paper discusses a novel spectral clustering algorithm called BVA_LSC (Barnes-Hut t-SNE Variational Autoencoder Landmark- based Spectral Clustering), which aims to improve the performance and efficiency of spectral clustering on high-dimensional datasets. The key contributions and methods presented in the paper are as follows:  Two-Pronged Feature Reduction: - Barnes-Hut t-SNE: This method is used for dimensionality reduction, which optimizes the computational cost by reducing the size of the similarity matrix used in spectral clustering. Barnes-Hut t-SNE is particularly effective for high-dimensional data. - Variational Autoencoder (VAE): A deep learning technique used alongside Barnes-Hut t-SNE to capture non-linear relationships in data and further reduce dimensionality.  Adaptive Landmark Selection: - K-harmonic means clustering: This algorithm is used initially to group data points and narrow down potential landmarks (a subset of representative data points). - Grey Wolf Optimization (GWO): An optimization algorithm inspired by the social hierarchy of grey wolves, which is used to select the most effective landmarks based on a novel objective function. This selection process ensures that the landmarks are evenly distributed across the dataset and represent the data well. 12
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  Related Studies  OptimizedSimilarity Matrix: - By reducing the number of features and carefully selecting landmarks, the algorithm decreases the size of the similarity matrix, which reduces the computational burden during eigen decomposition—a critical step in spectral clustering.  Dynamic Landmark Count Determination: - The paper introduces a new equation to dynamically determine the optimal number of landmarks based on the dataset’s features. This allows the algorithm to adapt to different datasets without requiring manual tuning.  Experimental Validation: - The algorithm was tested on several real-world datasets (e.g., MNIST, USPS, Fashion-MNIST) and compared against various state-of-the-art spectral clustering methods. The results showed that BVA_LSC generally outperforms other methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI), particularly for complex and high-dimensional datasets.  Computational Efficiency: - While BVA_LSC demonstrates superior clustering performance, it does so at the cost of slightly higher computational time compared to some of the other methods, especially as the number of landmarks increases.  Overall, the paper introduces a robust and efficient spectral clustering method that leverages advanced feature reduction and optimized landmark selection to tackle the challenges of high-dimensional data clustering. The approach balances accuracy with computational efficiency, making it suitable for large-scale data analysis tasks. 13
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  Conclusion and KeyTakeaways Powerful Insights from Data Clustering algorithms unlock hidden patterns and structures in complex data, enabling organizations to uncover valuable business insights. Adaptable to Various Domains From customer segmentation to image analysis, clustering techniques can be applied across a wide range of industries and use cases. Importance of Algorithm Selection Carefully choosing the right clustering algorithm based on data characteristics and business objectives is crucial for successful deployment. Continuous Improvement Evaluating clustering performance and iterating on models can lead to ongoing refinements and better decision-making support. 14