Getting started with simOutrank

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simOutrank clusters the traces of an event log by an outranking model of similarity. Instead of collapsing every perspective into a single distance, it scores each pair of traces on several problem-specific criteria – activities, transitions, duration, case attributes – and aggregates them with ELECTRE-III-style concordance and discordance. The result is a credibility matrix S that a spectral or hierarchical algorithm then partitions.

This vignette walks through the whole pipeline on the bundled illustrative_log, a 25-case customer-service log from Delias et al. (2023).

library(simOutrank)
head(illustrative_log)
#>   case_id activity           timestamp status satisfaction
#> 1       1        B 2021-01-01 00:01:00   GOLD         High
#> 2       1        E 2021-01-01 00:02:00   GOLD         High
#> 3       2        B 2021-01-01 00:01:00   GOLD         High
#> 4       2        E 2021-01-01 00:02:00   GOLD         High
#> 5       3        B 2021-01-01 00:01:00   GOLD         High
#> 6       3        E 2021-01-01 00:02:00   GOLD         High

1. Traces

as_traces() turns an event log into a traces object: the time-ordered, integer-encoded activity sequence of every case, plus the case-level attribute table.

traces <- as_traces(illustrative_log,
                    case_id = "case_id", activity = "activity",
                    timestamp = "timestamp")
traces
#> <traces>: 25 cases, 5 distinct activities
#>   trace length: min 2, median 5, max 7
#>   case attributes: status, satisfaction
summary(traces)[1:5, ]
#>   case_id n_events n_distinct
#> 1       1        2          2
#> 2       2        2          2
#> 3       3        2          2
#> 4       4        2          2
#> 5       5        2          2

2. Criteria

Criteria are built with the crit_* helpers. Each carries a direction (similarity or dissimilarity), a weight, and ELECTRE-III thresholds (indifference, similarity, and an optional veto). Here we mirror the four criteria of the source paper: what activities occur, in what order, and two case attributes.

criteria <- list(
  crit_activity_profile(weight = 0.2, indifference = 0.7, similarity = 0.8,
                        veto = 0.4),
  crit_edit_distance(weight = 0.2, similarity = 2, indifference = 3, veto = 6),
  crit_nominal("status", weight = 0.3),
  crit_nominal("satisfaction", weight = 0.3)
)
criteria[[1]]
#> <criterion 'activity_profile'>: direction = similarity, weight = 0.2
#>   indifference = 0.7, similarity = 0.8, veto = 0.4

Thresholds may be fixed numbers or quantiles of the criterion’s own value distribution via as_quantile(); the parameter-tuning vignette explores that choice.

3. Similarity

outrank_similarity() resolves any quantile thresholds, normalises the weights, and aggregates the per-criterion indices into S.

sim <- outrank_similarity(traces, criteria)
sim
#> <outrank_sim>: 25 cases, 4 criteria
#>   S in [0.000, 1.000], symmetric = TRUE
round(sim$S[1:5, 1:5], 2)
#>   1 2 3 4 5
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#> 4 1 1 1 1 1
#> 5 1 1 1 1 1

4. How many clusters?

eigengap() plots the smallest eigenvalues of the normalized Laplacian; a gap after the k-th value suggests k groups.

eigengap(sim, k_max = 8)

5. Cluster

The paper targets four natural groups (tier x satisfaction). Spectral clustering uses a seed for reproducible k-means.

clust <- cluster_traces(sim, k = 4, seed = 42)
clust
#> <outrank_clust>: 25 cases in 4 clusters (spectral)
#>   cluster sizes: 1=6, 2=8, 3=5, 4=6
split(names(clust$memberships), clust$memberships)
#> $`1`
#> [1] "11" "12" "13" "14" "15" "24"
#> 
#> $`2`
#> [1] "16" "17" "18" "19" "20" "21" "22" "23"
#> 
#> $`3`
#> [1] "6"  "7"  "8"  "9"  "10"
#> 
#> $`4`
#> [1] "1"  "2"  "3"  "4"  "5"  "25"

6. Use the result

Join memberships back onto the event log, inspect a cluster’s attribute profile, and compute validity indices.

augmented <- augment_log(clust, illustrative_log)
#> Joining on case-id column 'case_id'.
head(augmented)
#>   case_id activity           timestamp status satisfaction .cluster
#> 1       1        B 2021-01-01 00:01:00   GOLD         High        4
#> 2       1        E 2021-01-01 00:02:00   GOLD         High        4
#> 3       2        B 2021-01-01 00:01:00   GOLD         High        4
#> 4       2        E 2021-01-01 00:02:00   GOLD         High        4
#> 5       3        B 2021-01-01 00:01:00   GOLD         High        4
#> 6       3        E 2021-01-01 00:02:00   GOLD         High        4

plot(clust, type = "profile", attribute = "status")


if (requireNamespace("clValid", quietly = TRUE)) {
  validate_clusters(clust)
}
#> connectivity         dunn 
#>    20.276190     0.489198

Where next

Reproducing the illustrative example – the same pipeline framed as a published-result reproduction.
Robustness: constraints and trimming – inject domain knowledge and remove outliers.
Parameter tuning – weights, thresholds and k.

Reference

Delias, P., Doumpos, M., Grigoroudis, E. and Matsatsinis, N. (2023). Improving the non-compensatory trace-clustering decision process. International Transactions in Operational Research, 30(3), 1387–1406. doi:10.1111/itor.13062 ```

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.