k medoid clustering.pptx

Clustering methods

1. K medoid clustering Akhil wilson Mca21 Rollno:03

2. Introduction Clustering: Clustering is a type of unsupervised machine learning that involves grouping similar data points together into clusters. The goal of clustering is to partition a set of data points into groups (also called clusters) such that data points in the same group are more similar to each other than to those in other groups. Clustering is used in a wide range of applications, including market segmentation, image segmentation, document classification, and anomaly detection. The process of clustering can help to reveal patterns and relationships within the data, and can also be used to reduce the dimensionality of high-dimensional data. There are various algorithms for clustering, including K-means, K-medoids, hierarchical clustering, and density-based clustering. The choice of algorithm depends on the characteristics of the data and the requirements of the application.

3. 0verview of k-medoid clustering K-medoids is a clustering algorithm that groups similar data points together into clusters. It is a variation of the K-means algorithm and is used to partition a set of data points into K clusters, where K is a user-defined parameter. The K-medoids algorithm works by selecting K data points as the initial medoids, and then assigning each data point to the closest medoid. The medoid is defined as the data point that is closest to the other data points in the cluster. The algorithm then iteratively updates the medoids to minimize the sum of distances between data points and their cluster medoids. One of the key differences between K-medoids and K-means is that K-medoids is more robust to outliers, as the medoid is less influenced by individual data points than the cluster centroid used in K-means. This makes K-medoids a good choice for clustering data that is categorical or binary, or for data with a non-Euclidean distance metric. The K-medoids algorithm is computationally slower than K-means, as it requires finding the closest data point to the medoid. However, it is more flexible in terms of the distance metric used, as it can handle non-Euclidean distance metrics. K-medoids is a useful clustering algorithm for grouping similar data points into clusters and is particularly useful for categorical or binary data, or data with a non-Euclidean distance metric.

4. K medoids algorithm The K-medoids algorithm is a clustering algorithm that groups similar data points into K clusters, where K is a user-defined parameter. The algorithm works as follows: Step 1-> Initialization : Select K data points randomly as the initial medoids. Step 2-> Assignment : For each data point, calculate the distance to all K medoids and assign the data point to the closest medoid. This creates K clusters, each with a medoid and a set of data points assigned to it. Step 3-> Update: For each cluster, calculate the cost of replacing its medoid with each of its data points. If replacing the medoid with a data point results in a lower cost, update the medoid to that data point. Repeat this process until no further improvements can be made. Step 4-> Repeat steps 2 and 3 until the medoids no longer change or a maximum number of iterations is reached. Step 5-> Output The final set of K medoids and the data points assigned to each cluster. The K-medoids algorithm aims to minimize the sum of distances between data points and their cluster medoids. The distance metric used can be Euclidean, Manhattan, or any other user-defined distance metric. The choice of distance metric depends on the characteristics of the data and the requirements of the application.

5. Calculation of mediod The medoid of a cluster is defined as the data point that is closest to the other data points in the cluster. The calculation of medoid is a key step in the K-medoids algorithm, as the medoids are used to represent the cluster and to calculate the cost of updating the medoids. The medoid is typically calculated as follows: 1-:For each data point in the cluster, calculate the sum of distances between that data point and all other data points in the cluster. 2-:Select the data point with the minimum sum of distances as the medoid. This process is repeated for each cluster, so that a medoid is calculated for each cluster. The medoids are then used as the representative data points for the clusters, and are used to calculate the cost of updating the medoids in the next iteration of the algorithm. The calculation of medoid helps to ensure that the representative data points for the clusters are representative of the clusters as a whole, rather than being influenced by individual data points. This makes K-medoids a robust algorithm for clustering data with outliers or noisy data.

6. Advantages Robust to outliers: K-medoids is less sensitive to outliers than K-means, as the medoid is less influenced by individual data points than the cluster centroid used in K-means. Flexibility in distance metrics: K-medoids can handle non-Euclidean distance metrics, making it a good choice for clustering data with different types of features or data with a non-linear structure. Simple implementation: K-medoids is a simple and easy-to-understand algorithm, making it a good choice for clustering problems with a small number of data points or for small-scale applications. Faster convergence: K-medoids can converge faster than K-means, as it only requires calculating the distances between the data points and the medoids.

7. Disadvantages Computational cost: K-medoids is computationally more expensive than K-means, as it requires finding the closest data point to the medoid for each data point. Local minima: K-medoids is prone to getting stuck in local minima, meaning that it may not find the optimal solution for the clustering problem. Difficult to scale: K-medoids is not well suited for large-scale clustering problems, as the computational cost increases with the number of data points. Sensitive to initial medoids: The quality of the solution obtained by K-medoids is sensitive to the initial medoids selected, so care must be taken in selecting the initial medoids.

8. Applications of k medoid  K-medoids is a popular clustering algorithm that has a wide range of applications in various fields. Some of the common applications of K-medoids are: Customer Segmentation: K-medoids can be used to group customers based on their demographics, purchasing behavior, or other attributes, to better understand customer behavior and preferences. Image Segmentation: K-medoids can be used to segment images into different regions, based on the similarity of color, texture, or other features, to simplify image analysis and processing. Text Clustering: K-medoids can be used to cluster documents, articles, or other text data based on their content or topics, to categorize and summarize large amounts of text data. Anomaly Detection : K-medoids can be used to identify data points that are significantly different from other data points in the same cluster, to detect anomalies or outliers. Fraud Detection: K-medoids can be used to detect fraudulent transactions by grouping transactions based on their similarity, and identifying transactions that are significantly different from other transactions in the same cluster.

9. K medoid vs k means Similarities: Both algorithms are iterative: Both K-medoids and K-means use an iterative approach to refine the solution and find the optimal clustering solution. Both algorithms require the number of clusters to be specified in advance: In both algorithms, the user must specify the number of clusters to be formed. Both algorithms can be used with Euclidean distance metrics: Both K-medoids and K-means can use Euclidean distance metrics to calculate the similarity between data points.

10. Differences: Representation of clusters: K-medoids represents clusters using medoids, which are data points closest to other data points in the cluster, while K-means represents clusters using centroids, which are the mean of the data points in the cluster. Robustness to outliers : K-medoids is less sensitive to outliers than K-means, as the medoids are less influenced by individual data points than the centroids used in K-means. Computational cost: K-medoids is computationally more expensive than K-means, as it requires finding the closest data point to the medoid for each data point. Distance metric: K-medoids can handle non-Euclidean distance metrics, making it a good choice for clustering data with different types of features or data with a non-linear structure, while K-means is restricted to Euclidean distance metrics.

11. Conclusion Summary K-medoids is a clustering algorithm used for grouping similar data points into clusters. The algorithm works by selecting K representative data points (called medoids) from the data set and using these medoids to represent each cluster. The algorithm then iteratively reassigns data points to the closest medoid, and updates the medoids to the data point closest to the cluster mean. The process continues until the medoids no longer change. K-medoids is less sensitive to outliers compared to other clustering algorithms like K-means, and can handle non-Euclidean distance metrics, making it a good choice for clustering data with different types of features or data with a non-linear structure. The algorithm is computationally more expensive than K-means and requires the number of clusters to be specified in advance. K-medoids has a wide range of applications in various fields, including customer segmentation, image segmentation, text clustering, anomaly detection, fraud detection, gene expression analysis, and medical image analysis. In summary, K-medoids is a powerful clustering algorithm that can be used to group similar data points into clusters based on their similarity, and has a wide range of applications in various fields.

12. Future directions and possibilities K-medoids is a well-established and widely used clustering algorithm, and has been successfully applied to a variety of real-world problems. However, there are several areas where K-medoids could be improved and expanded in the future, including: Large scale clustering: With the increasing amount of data generated every day, there is a need for more efficient and scalable clustering algorithms. K-medoids could be optimized for large-scale clustering problems by developing more efficient algorithms for selecting medoids and updating the clusters. Non-uniform cluster size: K-medoids assumes that the size of each cluster is roughly the same, but in some real-world scenarios, the clusters can have varying sizes. There is a need for extensions of K-medoids that can handle non-uniform cluster sizes. Handling non-Euclidean distance metrics: K-medoids can handle non-Euclidean distance metrics, but there is still room for improvement in terms of efficiency and accuracy. Combination with other algorithms: K-medoids could be combined with other machine learning algorithms such as deep learning to improve the performance of the clustering process. High-dimensional data: Clustering high-dimensional data is challenging as the number of features can greatly affect the performance of clustering algorithms. There is a need for extensions of K-medoids that can handle high-dimensional data effectively. In conclusion, there is a lot of potential for further improvement and expansion of K-medoids in the future, and researchers and practitioners are working on addressing these challenges to make the algorithm more effective for a wider range of problems.