sai-likhith/k-means-clustering

Applying K Means Clustering Model Machine Learning model on open-source Amazon.com Clustering Dataset

K-Means-Clustering

Applying K Means Clustering Model Machine Learning model on open-source Amazon.com Clustering Dataset

• Unsupervised Learning
• Used for clustering, not for classification or regression
• K - Number
• Clusters K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into K distinct clusters. The goal is to group similar data points together and ensure that data points within the same cluster are more similar to each other than to those in other clusters. The algorithm accomplishes this by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the assigned points.

K-means clustering algorithm:

1. Initialization: Select the number of clusters, K, and randomly initialize K cluster centroids in the feature space.
2. Assignment: Assign each data point to the nearest centroid based on a distance metric, typically Euclidean distance. Each data point belongs to the cluster with the closest centroid.
3. Update: Recalculate the centroids of each cluster by taking the mean of all the data points assigned to that cluster.
4. Repeat steps 2 and 3 until convergence: Iterate steps 2 and 3 until the cluster assignments no longer change significantly or a maximum number of iterations is reached.
5. Finalization: Once the algorithm converges, the final centroids represent the cluster centers, and each data point is assigned to a specific cluster.

Advantages of K-means clustering:

• Simplicity: K-means is a relatively simple and intuitive algorithm to understand and implement. It is computationally efficient and can handle large datasets efficiently.
• Scalability: K-means clustering is scalable to large datasets as it has a linear computational complexity. It can be applied to a wide range of data sizes and dimensions.
• Versatility: K-means can be applied to various types of data and is not restricted to any specific data distribution. It is effective in finding clusters of different shapes and sizes.
• Interpretable results: The cluster centroids obtained from K-means are interpretable and can provide insights into the structure and patterns present in the data.

Limitations of K-means clustering:

• Dependency on initial centroid positions: K-means is sensitive to the initial placement of centroids. Different initializations can result in different cluster assignments and outcomes. To mitigate this, multiple runs with different initializations are often performed, and the best clustering solution is chosen.
• Fixed number of clusters: K-means requires the user to specify the number of clusters, K, in advance. If the true number of clusters is unknown, determining the appropriate value of K can be challenging. Incorrectly specified K may lead to suboptimal clustering results.
• Sensitive to outliers: K-means is sensitive to outliers as they can significantly impact the centroid calculation. Outliers may be assigned to inappropriate clusters or form their own clusters, affecting the overall clustering quality.
• Limited to linear boundaries: K-means assumes that clusters are isotropic, spherical, and have equal variance. It struggles to handle non-linear cluster boundaries and clusters of different shapes and sizes. Other clustering algorithms like DBSCAN or hierarchical clustering may be more appropriate for such scenarios.

Applications of K-means clustering:

• Customer segmentation: K-means clustering is widely used in market research to segment customers based on their behavior, preferences, or demographics. This helps businesses target specific customer groups with tailored marketing strategies.
• Image compression: K-means can be used to compress images by reducing the number of colors required to represent an image. By clustering similar colors together and using fewer colors to represent each cluster, image size can be reduced without significant loss of quality.
• Anomaly detection: K-means clustering can be employed for detecting anomalies or outliers in datasets. By considering data points that do not fit well into any cluster or are assigned to small clusters, it is possible to identify unusual or potentially suspicious instances. This is useful in fraud detection, network intrusion detection, and outlier analysis.
• Recommendation Systems: K-means clustering can contribute to building recommendation systems. By clustering users or items based on their preferences or behavior, it becomes possible to make personalized recommendations to users or group similar items together.
• Market Segmentation: K-means clustering is utilized in market research to segment markets or consumer populations. By clustering individuals or regions based on socioeconomic factors, purchasing behavior, or lifestyle, businesses can target specific segments with tailored marketing strategies.
• Social Network Analysis: K-means clustering can be used in social network analysis to identify communities or groups of individuals with similar characteristics or interaction patterns. It helps in understanding the structure and dynamics of social networks and can be applied in various domains such as marketing, sociology, and online social platforms.