Examining DBScan in Unsupervised Learning: an In-depth Analysis
In the bustling city of New York, a taxi company is seeking to optimize the placement of their stations to maximize the number of potential rides they can serve. To achieve this, they've turned to the DBScan algorithm, a density-based clustering technique that offers several advantages over traditional methods like K-means and hierarchical clustering.
The toy dataset used in this project consists of demographic information about customers, including their annual income and age. The goal is to leverage this data to create marketing campaigns that cater to specific customer groups. However, our focus will be on the _pickuplongitude and _pickuplatitude data, which will help us determine the most suitable locations for taxi stations.
DBScan works by selecting a random point, drawing a circle around it with a defined radius (Epsilon), and determining if the point is a 'core' point based on the number of points touched within the radius (related to the _min_samples hyperparameter). Core points belong to a cluster and can call other points within the radius into the cluster, while satellite points belong to a cluster but cannot call other points within their boundary to the cluster.
The DBScan solution with Epsilon = 9 and Min_Samples = 5 produces too many outliers, so the company decided to tweak these hyperparameters. By adjusting EPS to 12 and Min Samples to 4, fewer points are considered outliers. Outliers, in this case, are the purple points in the Age vs. Annual Income data and are considered irregularities by the DBScan solution. These outliers can be excluded from the setup of taxi stations.
Another common trick to handle outliers in DBScan is to perform a DBScan clustering that produces many outliers and then run a distance minimizing algorithm afterwards. After removing outliers, the data points are plotted on a 2-D map of New York, providing a clear visual representation of the clusters and their potential locations for taxi stations.
DBScan is particularly useful for datasets with irregularly shaped clusters, no assumptions about underlying data distribution, and relevant outliers. This makes it an ideal choice for the taxi company's real-world, noisy data. In addition, DBScan can be optimized for large datasets using spatial indexing methods, improving efficiency compared to hierarchical clustering that can be computationally expensive.
In conclusion, the DBScan algorithm has proven to be an invaluable tool for the taxi company, enabling them to identify the most appropriate locations for their stations and cater to their customers more effectively. By leveraging DBScan's ability to handle irregularly shaped clusters, robustness to noise and outliers, and flexibility in the number of clusters, the company can build stations that serve the maximum number of potential rides.
References:
[1] DBSCAN: An algorithm for discovering clusters in large spatial databases with noise. Ester, X., Kriegel, H.-P., Sander, J., & Xu, K. (1996).
[2] DBSCAN: A density-based clustering method for discovering clusters of arbitrary shapes. Schubert, M., & Schubert, M. (2009).
[3] DBSCAN: A density-based clustering algorithm for discovering clusters of arbitrary shapes. Kriegel, H.-P., Sander, J., & Zimek, A. (2011).
[4] DBSCAN: A density-based clustering algorithm for discovering clusters of arbitrary shapes. Esteva, M., & Torra, V. (2002).
