5 points of DBSCAN and OPTICS: DBSCAN: 1. checks for each data point sequentially to determine the point type. 2. It might occupy the data point which may be a part of the next cluster because it checks the data points sequentially. (disadvantage) 3. It can be good for Arbitrary Datasets and determine varying densities. (Advantage) 4. It can handle outliers very well. (Advantage) 5. It is efficient as it checks the whole dataset's data points thoroughly only once, unlike the other algorithms which iteratively check the data points.-----------------------------------------------------------------OPTICS: 1. Uses Min heap and makes a cluster according to each neighboring data point. (Advantage) 2. Can Extract clusters of varying densities and shapes. (Advantage) 3. Uses more storage as it stores data of the queues and is more computationally expensive as it needs to check each neighboring data point.(Disdvantage) 4. It doesn't need a Fixed Epsilon Parameter. (Advantage) 5. It is more flexible in selecting the number of clusters. (Advantage)

OPTICS (Ordering Points To Identify the Clustering Structure) offers several advantages over traditional clustering methods like DBSCAN. Here are three main benefits: 1. No Need for a Fixed Epsilon Parameter 2. Ability to Handle Varying Densities 3. Hierarchical Clustering Structure

Optics: Ordering points to identify the clustering structure, no need number of cluster, high computational power increaseDBscan is a good choice for datasets with arbitrary shaped clusters and handle noise and outlier while OPTICS is more flexible in selecting number of clusters and can extract cluster of varying densities and shapes. It can extract cluster of varying densities but it take high computation.

Done

Lecture done