Online Clustering for a Number Stream

You are given a number stream x1, x2, ... (one integer/float arrives at a time). Design and implement an online clustering algorithm. Interview requirements: You may choose any clustering approach (e.g., online k-means, threshold-based incremental clustering, sliding-window clustering) and justify your choice. Implement the core logic: when a new value x arrives, assign it to a cluster and update that cluster’s statistics (e.g., center/mean, count). Output: for each incoming x, output the assigned cluster_id (0-indexed). You must define (and state) the following choices Whether the number of clusters is fixed (k) or can grow dynamically. Distance metric (e.g., 1D Euclidean distance |x - center|). Initialization strategy. (Optional) sliding window or decay factor to handle drift. Suggested input Line 1: if using fixed k, an integer k (otherwise omitted). Line 2: integer n. Line 3: n numbers (or n lines each one number). Output n lines, each a cluster_id. Example Input 2 6 1 2 10 11 12 3 Output 0 1 1 1 1 0