Outranking-Based Trace Clustering

Implements outranking-based trace clustering for process mining. Pairwise similarity between traces is assessed on multiple, problem-specific criteria using ELECTRE-III-style partial concordance and discordance indices with indifference, similarity and veto thresholds. The aggregated credibility matrix is then clustered using normalized spectral clustering or hierarchical clustering. Also includes outlier trimming, must-link/cannot-link adjustments, validation indices and diagnostic plots.

simOutrank

Outranking-based trace clustering for process mining.

simOutrank clusters the traces of an event log by an outranking model of similarity. Instead of collapsing every perspective into one 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 (indifference, similarity and veto thresholds). The resulting credibility matrix is partitioned by normalized spectral or hierarchical clustering, with optional outlier trimming and must-link / cannot-link constraints.

Installation

# install.packages("remotes")
remotes::install_github("pdelias/simOutrank")

A 20-line example

library(simOutrank)

# 1. An event log -> a traces object (bundled illustrative log, 25 cases).
traces <- as_traces(illustrative_log,
                    case_id = "case_id", activity = "activity",
                    timestamp = "timestamp")

# 2. Criteria over several perspectives (weights normalised internally).
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)
)

# 3. Aggregate to a credibility matrix, choose k, and cluster.
sim   <- outrank_similarity(traces, criteria)
eigengap(sim, k_max = 8)                       # how many clusters?
clust <- cluster_traces(sim, k = 4, seed = 42)
split(names(clust$memberships), clust$memberships)

Learn more

Getting started — the full pipeline, step by step.
Reproducing the illustrative example and Robustness — reproduce the published results, then inject domain knowledge and trim outliers.
Parameter tuning — thresholds, weights and k, with sensitivity sweeps.

See browseVignettes("simOutrank") or the package website for these, plus website-only articles that reproduce the emergency-room study and cluster a large log.

Reference

Delias, P., Doumpos, M., Grigoroudis, E. and Matsatsinis, N. (2019). A non-compensatory approach for trace clustering. International Transactions in Operational Research, 26(5), 1828–1846. doi:10.1111/itor.12395
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

Design and roadmap: design/DESIGN.md.