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In this approach, we treat each string as a node in a graph. Two nodes are connected if the corresponding strings are similar. We use a Union-Find data structure to efficiently group the strings into connected components based on similarity.
Time Complexity: O(n^2 * k) where n is the number of strings and k is the average length of the strings.
Space Complexity: O(n), storing the parent array.
1function find(parent, x) {
2 if (parent[x] !== x) parent[x] = find(parent, parent[x]);
3 return parent[x];
4}
5
6function union(parent, x, y) {
7 const rootX = find(parent, x);
8 const rootY = find(parent, y);
9 if (rootX !== rootY) parent[rootY] = rootX;
10}
11
12function areSimilar(a, b) {
13 let diff = 0;
14 for (let i = 0; i < a.length; i++) {
15 if (a[i] !== b[i]) {
16 diff++;
17 if (diff > 2) return false;
18 }
19 }
20 return diff === 2 || diff === 0;
21}
22
23function numSimilarGroups(strs) {
24 const n = strs.length;
25 const parent = Array.from({ length: n }, (_, i) => i);
26
27 for (let i = 0; i < n; i++) {
28 for (let j = i + 1; j < n; j++) {
29 if (areSimilar(strs[i], strs[j])) {
30 union(parent, i, j);
31 }
32 }
33 }
34
35 const groups = new Set();
36 for (let i = 0; i < n; i++) {
37 groups.add(find(parent, i));
38 }
39
40 return groups.size;
41}
42
43// Example usage:
44const strs = ["tars", "rats", "arts", "star"];
45console.log(numSimilarGroups(strs));
46This JavaScript solution employs the Union-Find technique to group strings based on similarity criteria. Strings that differ by two characters or less can be merged into the same group. The function areSimilar() assists in determining if two strings are similar.
This approach considers each string as a node and treats detecting similarities like exploring components in a graph. We use a depth-first search (DFS) algorithm to explore nodes. If two strings are similar, we explore all strings similar to this pair within a recursive call.
Time Complexity: O(n^2 * k) where n is the number of strings and k is the length of strings.
Space Complexity: O(n) to maintain the visited array.
1
This Java DFS-based solution explores all connected components in the similarity graph. Upon visiting an unvisited node, the dfs function delves into all connected nodes, ensuring complete exploration of the component leading to a count of distinct groups.