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group/cluster similar strings.


Andrew Jeffery <>



strgrp clusters similar strings using the Longest Common Subsequence (LCS) algorithm[1]. Clustering is governed by a threshold value, which is compared with the normalised LCS scores calculated from the input string and each existing group.

As a coarse and not entirely accurate summary, strgrp takes the following steps:

1. For all known groups, calculate the normalised LCS value against the input string

2. Find the maximum normalised LCS value and associated group

3. If the calculated maximum normalised LCS value exceeds the configured threshold add the input string to the group, otherwise create a new group

The clustering operation is expensive; LCS on its own is computationally O(m * n) on its two input strings and optimally requires O(min(m, n)) memory. In general each input string should be compared against all known strings, giving O(n^2) behaviour of the clustering algorithm on top of the O(m * n) LCS similarity measurement.

strgrp tries to battle this complexity on several fronts:

1. Caching of input strings and their associated group. By incurring the cost of a map's string hash function we may eliminate all further search costs for exact matches, potentially reducing the insertion to a constant-time operation.

2. Coarse reduction of the required comparisons. Each group has a 'key', which is the string that triggered the creation of the group. Input strings are only compared against group keys rather than all known strings, reducing the complexity to the current number of groups rather than all known strings. Note due the pathological case where the number of groups is equal to the number of known strings the algorithm still has O(n^2) computational complexity

3. Whilst the data dependencies of LCS prevent internally parallel implementations, LCS and other filters can be applied in parallel. The code uses OpenMP to automatically distribute scoring of the input string against group keys across a number of threads.

4. Elimination of LCS computations that will never breach the configured threshold. Two measurements are used as rejecting filters (i.e. a failure to exceed the threshold prevents further measurements being made):

4a. Comparsion of string lengths: String lengths must be within given bounds to satisfy the user-supplied similarity constraint. A negative result avoids invoking the O(m * n) behaviour of LCS at the cost of O(m + n) in the two strlen() invocations.

4b. Comparison of character distribution: Cosine similarity[2] is used to measure unordered character similarity between the input strings. The implementation is again O(m + n), and avoids the O(m * n) behaviour of LCS.

Performance will vary not only with the number of input strings but with their lengths and relative similarities. A large number of long input strings that are relatively similar will give the worst performance. However to provide some context, strgrp can cluster a real-world test set of 3500 strings distributed in length between 20 and 100 characters to 85% similarity on a 4-core 2010-era amd64 laptop in approximately 750ms.




FILE *f;
char *buf;
struct strgrp *ctx;
struct strgrp_iter *iter;
const struct strgrp_grp *grp;
struct strgrp_grp_iter *grp_iter;
const struct strgrp_item *item;

f = fdopen(0, "r");
#define BUF_SIZE 512
buf = malloc(BUF_SIZE);
ctx = strgrp_new(0.85);
while(fgets(buf, BUF_SIZE, f)) {
    buf[strcspn(buf, "\r\n")] = '\0';
    if (!strgrp_add(ctx, buf, NULL)) {
        printf("Failed to classify %s\n", buf);

// Re-implement something similar to strgrp_print() via API as an
// example
iter = strgrp_iter_new(ctx);
while(NULL != (grp = strgrp_iter_next(iter))) {
    printf("%s\n", strgrp_grp_key(grp));
    grp_iter = strgrp_grp_iter_new(grp);
    while(NULL != (item = strgrp_grp_iter_next(grp_iter))) {
         printf("\t%s\n", strgrp_item_key(item));