Vol-3194/paper52
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| Paper | |
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 edit
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| id | Vol-3194/paper52 |
| wikidataid | Q117344962→Q117344962 |
| title | Correlation Clustering: from Local to Global Constraints |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper52.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/MandaglioTG22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Correlation Clustering: from Local to Global Constraints
Correlation Clustering: from Local to Global
Constraints
(Discussion Paper)
Domenico Mandaglio1 , Andrea Tagarelli1 and Francesco Gullo2
1
DIMES Dept., University of Calabria, 87036 Rende (CS), Italy
2
UniCredit, Rome, Italy
Abstract
Given a set of data objects, consider that object pairs are assigned two weights expressing the advantage
of putting those objects in the same cluster or in separate clusters, respectively. Correlation clustering
partitions the input object set so as to minimize the sum of the intra-cluster negative-type weights plus
the sum of the inter-cluster positive-type weights. Existing approximation algorithms provide quality
guarantees if the weights are bounded in some way. Regardless of the type, the weight bounds that
have been so far studied are local bounds, i.e., constraints that are required to hold for every object pair
in isolation. In this paper, we discuss global weight bounds in correlation clustering, and in particular,
we derive bounds on edge weights’ aggregate functions that are sufficient to lead to proved quality
guarantees. Our formulation extends the range of applicability of the most prominent existing correlation-
clustering algorithms thus providing benefits, both theoretical and practical. Also, we showcase our
results in a real-world scenario of feature selection for fair clustering.
Keywords
min-disagreement correlation clustering, probability constraint, fair clustering
1. Introduction
Correlation clustering [1] is an important clustering formulation that has received considerable
attention from theoreticians and practitioners, and found application in several contexts [2].
Given a set of objects and nonnegative real weights expressing “positive” and “negative”
feeling of clustering any two objects together, min-disagreement correlation clustering (Min-CC)
partitions the input object set so as to minimize the sum of the intra-cluster negative-type
weights plus the sum of the inter-cluster positive-type weights. Min-disagreement correlation
clustering is APX-hard, but efficient constant-factor approximation algorithms exist if the
weights are bounded in some way. The weight bounds tackled so far in the literature are said
local, as they are required to hold for every single object pair.
In this paper, we discuss the main theoretical and experimental results from our study
in [3], where we introduced the problem of min-disagreement correlation clustering with
global weight bounds, i.e., constraints to be satisfied by the input weights altogether. Our main
contribution is a sufficient condition that establishes when any algorithm achieving a certain
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ d.mandaglio@dimes.unical.it (D. Mandaglio); andrea.tagarelli@unical.it (A. Tagarelli);
francesco.gullo@unicredit.eu (F. Gullo)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
�Algorithm 1 Pivot [4]
Input: Graph 𝐺 = (𝑉, 𝐸); nonnegative weights 𝑤𝑒+ , 𝑤𝑒− , ∀𝑒 ∈ 𝐸
Output: Clustering 𝒞 of 𝑉
1: 𝒞 ← ∅, 𝑉 ′ ← 𝑉
2: while 𝑉 ′ ̸= ∅ do
3: pick a pivot vertex 𝑢 ∈ 𝑉 ′ uniformly at random
4: add 𝒞𝑢 = {𝑢} ∪ {𝑣 ∈ 𝑉 ′ | (𝑢, 𝑣) ∈ 𝐸, 𝑤𝑢𝑣+ −
> 𝑤𝑢𝑣 } to 𝒞 and remove 𝐶𝑢 from 𝑉 ′
approximation under the probability constraint keeps the same guarantee on an input that
violates the constraint. This extends the range of applicability of the most prominent existing
correlation-clustering algorithms, including the popular Pivot, thus providing both theoretical
and practical benefits. Experiments have shown the usefulness of our approach, in terms of
both worthiness of employing existing efficient algorithms, and guidance on the definition of
weights from feature vectors in a task of fair clustering.
2. Correlation Clustering with local weight bounds
The input of correlation clustering is a set 𝑉 of objects, and two nonnegative, real-valued weights
+ , 𝑤 − for every (unordered) object pair 𝑢, 𝑣 ∈ 𝑉 . Any “positive” 𝑤 + (resp. “negative” 𝑤 − )
𝑤𝑢𝑣 𝑢𝑣 𝑢𝑣 𝑢𝑣
weight expresses the benefit of clustering 𝑢 and 𝑣 together (resp. separately). This input can
equivalently be represented as a graph 𝐺 with vertex set 𝑉 and edge weights 𝑤𝑢𝑣 + , 𝑤 − , for all
𝑢𝑣
𝑢, 𝑣 ∈ 𝑉 , and with edge (𝑢, 𝑣) being drawn only if at least one among 𝑤𝑢𝑣 and 𝑤𝑢𝑣
+ − is nonzero.
In this work we tackle the problem of min-disagreement correlation clustering:
Problem 1 (Min-CC [4]). Given an undirected graph 𝐺 = (𝑉, 𝐸), with vertex set 𝑉 and edge
set 𝐸 ⊆ 𝑉 × 𝑉 , and nonnegative weights 𝑤𝑒+ , 𝑤𝑒− ∈ R+ 0 for all edges 𝑒 ∈ 𝐸, find a clustering
(i.e., an injective function expressing cluster-membership) 𝒞 : 𝑉 → N+ that minimizes
∑︁ ∑︁
−
𝑤𝑢𝑣 + +
𝑤𝑢𝑣 . (1)
(𝑢,𝑣)∈𝐸,𝒞(𝑢)=𝒞(𝑣) (𝑢,𝑣)∈𝐸,𝒞(𝑢)̸=𝒞(𝑣)
For the sake of presentation, we assume 𝑤𝑒+ = 𝑤𝑒− = 0, for all 𝑒 ∈/ 𝐸, and non-trivial Min-CC
instances, i.e., 𝑤𝑒 ̸= 𝑤𝑒 , for some 𝑒 ∈ 𝐸.
+ −
Min-CC is NP-hard [1, 5] yet difficult to approximate, being it APX-hard even for com-
plete graphs and edge weights (𝑤𝑒+ , 𝑤𝑒− ) ∈ {(0, 1), (1, 0)}, ∀𝑒 ∈ 𝐸 [6]. For general (i.e., not
necessarily complete) graphs and unconstrained weights, the best known approximation factor
is 𝒪(log |𝑉 |) [6, 7]. This factor improves if restrictions on edge weights are imposed. The one
that has received remarkable attention is the probability constraint (pc): A Min-CC instance
is said to satisfy the probability constraint if 𝑤𝑢𝑣
+ + 𝑤 − = 1, for all pairs 𝑢, 𝑣 ∈ 𝑉 .
𝑢𝑣
A Min-CC instance obeying the pc necessarily corresponds to a complete graph (otherwise,
any missing edge would violate the pc). Under the pc, Min-CC admits constant-factor guarantees.
The best known approximation factor is 4, achievable – as shown in [8] – by Charikar et al.’s
algorithm [6]. That algorithm is based on rounding the solution to a large linear program (with
a number Ω(|𝑉 |3 ) of constraints), thus being feasible only on small graphs.
�Algorithm 2 GlobalCC
Input: Graph 𝐺 = (𝑉, 𝐸); nonnegative weights 𝑤𝑒+ , 𝑤𝑒− , ∀𝑒 ∈ 𝐸, satisfying Theorem 1; algorithm A
achieving 𝛼-approximation guarantee for Min-pc-CC
Output: Clustering 𝒞 of 𝑉
1: choose 𝑀, 𝛾 s.t. 𝑀 +
𝛾 ∈ [Δ𝑚𝑎𝑥 , 𝑎𝑣𝑔 + 𝑎𝑣𝑔 ]
−
{Theorem 1}
2: compute 𝜎𝑢𝑣 = 𝛾 (𝑤 +
(︀ 𝑢𝑣 + 𝑤𝑢𝑣 ) −)︀ 𝑀 , ∀𝑢, 𝑣 ∈ 𝑉
−
3: compute 𝜏𝑢𝑣
±
=𝑀 1 ±
𝛾 𝑤𝑢𝑣 − 𝜎2𝑢𝑣 , ∀𝑢, 𝑣 ∈ 𝑉 (using 𝑀, 𝛾 defined in Step 1)
4: 𝒞 ← run A on Min-pc-CC instance ⟨𝐺′ = (𝑉, 𝑉 × 𝑉 ), {𝜏𝑒+ , 𝜏𝑒− }𝑒∈𝑉 ×𝑉 ⟩
Here, we are particularly interested in the Pivot algorithm [4], due to its theoretical properties
– it achieves a factor-5 expected guarantee for Min-CC under the pc – and practical benefits – it
takes 𝒪(|𝐸|) time, and is easy-to-implement. Pivot simply picks a random vertex 𝑢, builds a
cluster as composed of 𝑢 and all the vertices 𝑣 such that an edge with 𝑤𝑢𝑣 + > 𝑤 − exists, and
𝑢𝑣
removes that cluster. The process is repeated until the graph has become empty (Algorithm 1).
3. Correlation Clustering with global weight bounds
The weight bounds that have been so far studied are local bounds, i.e., constraints that are
required to hold for every object pair in isolation. In this work, we are the first to consider global
weight bounds in min-disagreement correlation clustering. We derive bounds on edge weights’
aggregate functions that are sufficient to lead to proved quality guarantees. More specifically,
for a Min-CC instance ⟨𝐺 = (𝑉, 𝐸), {𝑤𝑒+ , 𝑤𝑒− }𝑒∈𝐸 ⟩ we define:
(︁ )︁−1 ∑︀ (︁ )︁−1 ∑︀
𝑎𝑣𝑔 + = |𝑉2 | 𝑒∈𝐸 𝑒𝑤 +, 𝑎𝑣𝑔 − = |𝑉 |
2
−
𝑒∈𝐸 𝑤𝑒 , Δ𝑚𝑎𝑥 = max𝑒∈𝐸 |𝑤𝑒+ − 𝑤𝑒− |
The main theoretical result of this work is described by the following theorem.
Theorem 1. If the condition 𝑎𝑣𝑔 + + 𝑎𝑣𝑔 − ≥ Δ𝑚𝑎𝑥 holds for a Min-CC instance 𝐼, then it is
possible to construct a Min-CC instance 𝐼 ′ (in linear time and space) such that (𝑖) the probability
constraint holds on 𝐼 ′ , and (𝑖𝑖) an 𝛼-approximate clustering on 𝐼 ′ (i.e., a clustering whose objective-
function value is no more than 𝛼 times 𝐼 ′ ’s optimum) is an 𝛼-approximate clustering on 𝐼 too.
Let Min-pc-CC denote the version of Min-CC operating on instances that satisfy the pc.
According to Theorem 1, if 𝑎𝑣𝑔 + + 𝑎𝑣𝑔 − ≥ Δ𝑚𝑎𝑥 holds for a Min-CC instance, then any
𝛼-approximation algorithm A for Min-pc-CC can be employed – as a black box – to get an
𝛼-approximate solution to that Min-CC instance. The algorithm for doing so is simple: given
a Min-CC instance 𝐼, get a Min-pc-CC instance via strict approximation-preserving (sap)
reduction, and run the black-box algorithm A on it (Algorithm 2). Being the reduction sap,
Algorithm 2 on input ⟨𝐼, A⟩ achieves factor-𝛼 guarantee on 𝐼.
A noteworthy consequence of this result is that, if a Min-CC instance 𝐼 satisfies our condition,
then the Pivot algorithm can be used to get (in linear time and space) a clustering achieving
a 5-approximation guarantee on 𝐼.1 This corresponds to extending the range of validity of
1
A probability-constraint-compliant Min-CC instance 𝐼 ′ is derivable from 𝐼 in linear time and space (cf. Th. 1 (𝑖)). Pivot on
𝐼 ′ yields a 5-approximate clustering [4]. A 5-approximate clustering on 𝐼 ′ is a 5-approximate clustering on 𝐼 (cf. Th. 1 (𝑖𝑖)).
�Pivot’s guarantee beyond the probability constraint: our global-weight-bounds condition now
suffices for the 5-approximation to hold. A key advantage is that our condition is more likely to
be satisfied than the probability constraint; for instance, it may happen that a bunch of edges
are missing in the graph (thus violating the probability constraint), but, if our condition holds,
one can get a 5-approximate clustering with Pivot. We point out that our result is general and
holds for any Min-CC algorithm achieving approximation guarantees under the probability
constraint. However, the contextualization to the Pivot algorithm is relevant and worth to be
exploited, since Pivot achieves the best tradeoff between quality guarantees and efficiency.
4. Analysis of the global-weight-bounds condition
Our result can be exploited to quickly yet easily recognize whether employing probability-
constraint-aware approximation algorithms is a worth choice even if the probability constraint
is not met. As an example, consider a graph that violates the probability constraint. So far,
that graph would have likely been handled with linear-programming (LP) algorithms [6, 7], as
they achieve (factor-𝒪(log |𝑉 |)) approximation guarantees on general graphs/weights (whereas
algorithms like Pivot are just heuristics if the probability constraint does not hold). Instead,
our condition can be used as an indicator of whether Pivot can still achieve guarantees even if
the probability constraint is violated, thus being preferred over the less efficient LP algorithms.
Our experiments confirmed this theoretical finding, i.e. a better fulfilment of our condition
corresponds to better performance of Pivot with respect to the LP algorithms, and vice versa.
Settings. We selected four real-world graphs, namely Karate, Dolphins, Adjnoun, and Football.2
Note that the small size of such graphs is not an issue because this evaluation stage involves,
among others, LP correlation-clustering algorithms, whose Ω(|𝑉 |3 ) time complexity makes them
unaffordable for graphs larger than that. We augmented these graphs with artificially-generated
edge weights, to test different levels of fulfilment of our global-weight-bounds condition stated in
Theorem 1. We controlled the degree of compliance of the condition by a target ratio parameter,
defined as 𝑡 = Δ𝑚𝑎𝑥 /(𝑎𝑣𝑔 + + 𝑎𝑣𝑔 − ). The condition is satisfied if and only if 𝑡 ∈ [0, 1], and
smaller target-ratio values correspond to better fulfilment of the condition, and vice versa.
Given a desired target ratio, edge weights are generated as follows. First, all weights are
drawn uniformly at random from a desired [𝑙𝑏, 𝑢𝑏] range. Then, the weights are adjusted in
a two-step iterative fashion, until the desired target ratio is achieved: (𝑖) the maximum gap
Δ𝑚𝑎𝑥 is fixed, the weights are changed for pairs that do not contribute to Δ𝑚𝑎𝑥 so as to reflect
a change in 𝑎𝑣𝑔 + , 𝑎𝑣𝑔 − ; (𝑖𝑖) 𝑎𝑣𝑔 + , 𝑎𝑣𝑔 − are fixed, Δ𝑚𝑎𝑥 is updated by randomly modifying
pairs that contribute to Δ𝑚𝑎𝑥 . Finally, weight pairs are randomly assigned to the edges.
We compared the performance of Pivot (Algorithm 1 [4]) to one of the state-of-the-art
algorithms achieving factor-𝒪(log |𝑉 |) guarantee on general graphs/ weights [6]. We dub the
latter LP+R, alluding to the fact that it rounds the solution of a linear program. We evaluated
correlation-clustering objective, number of output clusters, and runtimes of these algorithms.
Results. Figure 1 shows the quality (i.e., Min-CC objective) of the produced clusterings, with the
bottom-left insets reporting the ratio between the performance of Pivot and LP+R. Results refer
to target ratios 𝑡 varied from [0, 3], with stepsize 0.1, and weights generated with 𝑙𝑏 = 0, 𝑢𝑏 = 1.
2
Publicly available at http://konect.cc/networks/
� 75 425 600
Pivot 150 Pivot Pivot Pivot
70 LP+R LP+R 400 LP+R 550 LP+R
Min-CC obj.
Min-CC obj.
Min-CC obj.
Min-CC obj.
65 140
375
60 130 500
1.025 1.025 350 1.025 1.000
55 1.000 120 1.000 450 0.975
0.975 325 1.000
50 0.1 1 2 3 110 0.1 1 2 3 0.1 1 2 3 0.1 1 2 3
400
0.1 0.5 1.0 1.5 2.0 2.5 3.0 0.1 0.5 1.0 1.5 2.0 2.5 3.0 0.1 0.5 1.0 1.5 2.0 2.5 3.0 0.1 0.5 1.0 1.5 2.0 2.5 3.0
Target ratio Target ratio Target ratio Target ratio
(a) Karate (b) Dolphins (c) Adjnoun (d) Football
Figure 1: Min-CC objective by varying the target ratio [3].
For each target ratio, all measurements correspond to averages over 10 weight-generation runs,
and each of such runs corresponds to averages over 50 runs of the tested algorithms.
The main goal here is to have experimental evidence that a better fulfilment of our global
condition leads to Pivot’s performance closer to LP+R’s one, and vice versa. Figure 1 confirms
the above: in all datasets, Pivot performs more closely to LP+R as the target ratio gets smaller.
In general, Pivot performs similarly to LP+R for 𝑡 ∈ [0, 1], while being outperformed for 𝑡 > 1.
This conforms with the theory: on these small graphs, factor-5 Pivot’s approximation is close
to factor-𝒪(log |𝑉 |) LP+R’s approximation. Pivot achieves the best performance on Football,
where it outperforms LP+R even if the condition is not met. This is motivated by Football’s
higher clustering coefficient and average degree, which help Pivot sample vertices (and, thus,
build clusters) in dense regions of the graph. This is confirmed by the number of clusters
(Table 1-(left)): Pivot yields more clusters than LP+R on all datasets but Football.
Concerning execution times, Pivot runs in less than one second, and as expected, it is extremely
faster than LP+R, whose runtimes are about 2 seconds in Karate, 37 in Dolphins, and above 770
in Adjnoun and Football.3 The inefficiency of LP+R further emphasizes the importance of our
result in extending the applicability of faster algorithms like Pivot.
We complement this stage of evaluation by testing different graph densities, and for target
ratios 𝑡 = 1 (borderline satisfaction of our condition) and 𝑡 = 20 (far fulfilment of the condition).
Again, the results (not shown) meet the expectations: in terms of clustering quality, Pivot
performs closely to or better than LP+R for 𝑡 = 1, while the opposite happens for 𝑡 = 20.
Denser graphs correspond to better Pivot performance. This is again explained since higher
densities favor better Pivot’s random choices. Runtimes are not affected by graph density. This
is expected as well, as LP+R runtimes are dominated by the time spent in building and solving
the linear program, which depends on the number of vertices only, whereas variations in the
runtimes of Pivot cannot be observed due to the small size of the datasets at hand.
5. Application to fair clustering
An important exploitation of our theoretical results concerns the selection of features that lead
edge weights to express the best tradeoff between an accurate representation of the objects and
the suitability of the correlation-clustering weights to ensure approximation guarantees. Our
global-weight-bounds condition can be an effective way to the achievement of this tradeoff, and
it can be fulfilled more easily than local weight bounds (e.g., in case of probability constraint, it
3
Experiments were carried out on the Cresco6 cluster (https://www.eneagrid.enea.it)
�Table 1
Average clustering-sizes for various target ratios on the real-world graph datasets (left) and main
characteristics of the relational datasets used in the fair clustering scenario (right).
#objs. #attrs. fairness-aware (sensitive) attributes
0.1 0.5 1 3
Pivot LP+R Pivot LP+R Pivot LP+R Pivot LP+R race, sex, country, education, occupation,
Adult 32 561 7/8
marital-status, workclass, relationship
Karate 21.75 17.18 29.61 27.93 27.22 24.66 28.17 26.81
Bank 41 188 18/3 job, marital-status, education
Dolphins 49.25 50.59 45.3 38.67 49.57 44.45 48.89 43.66
Credit 10 127 17/3 gender, marital-status, education-level
Adjnoun 70.35 65.93 80.97 75.86 90.76 84.93 91.27 79.78
sex, male_edu, female_edu,
Football 64.43 84.91 77.14 96.43 68.35 78.72 90.87 100.31 Student 649 28/5
male_job, female_job
is hard to find a subset of features leading to positive-type and negative-type weights summing
exactly to one for all the object pairs). We showcase this capability in a task of fair clustering.
Let 𝒳 be a set of objects defined over a set of attributes 𝒜, which is assumed to be partitioned
into two sets: 𝒜𝐹 , which contains fairness-aware or sensitive attributes (e.g., gender, race,
religion), and 𝒜¬𝐹 , which includes the remaining, non-sensitive attributes. We also assume that
part of the attributes might be numerical, and the others as categorical; we will use superscripts
𝑁 and 𝐶 to distinguish the two types, therefore 𝒜𝐹 = 𝒜𝐹𝑁 ∪ 𝒜𝐹𝐶 and 𝒜¬𝐹 = 𝒜¬𝐹 𝑁 ∪ 𝒜𝐶 .
¬𝐹
We consider a twofold fair-clustering objective: cluster the objects such that (𝑖) the intra-
cluster similarity and the inter-cluster similarity are maximized and minimized, respectively,
according to the non-sensitive attributes; (𝑖𝑖) the intra-cluster similarity and the inter-cluster
similarity are minimized and maximized, respectively, according to the sensitive attributes.
Pursuing this second objective would help distribute similar objects (in terms of sensitive
attributes) across different clusters, thus helping the formation of diverse clusters. This is
beneficial to ensure that the distribution of groups defined on sensitive attributes within each
cluster approximates the distribution across the dataset.
The task of fair clustering can be mapped to a Min-CC instance where the positive-type and
negative-type weights, respectively, can be defined as follows:
(︁ )︁
¬𝐹 ¬𝐹
+
𝑤𝑢𝑣 := 𝜓 + 𝛼𝑁 · 𝑠𝑖𝑚𝒜¬𝐹 (𝑢, 𝑣) + (1 − 𝛼𝑁 ) · 𝑠𝑖𝑚𝒜¬𝐹 (𝑢, 𝑣) (2)
𝑁 𝐶
(︁ )︁
−
𝑤𝑢𝑣 := 𝜓 − 𝛼𝑁 𝐹
· 𝑠𝑖𝑚𝒜𝐹 (𝑢, 𝑣) + (1 − 𝛼𝑁𝐹
) · 𝑠𝑖𝑚𝒜𝐹 (𝑢, 𝑣) (3)
𝑁 𝐶
where 𝛼𝑁 𝐹 = |𝒜𝐹 |/(|𝒜𝐹 |+|𝒜𝐹 |) and 𝛼¬𝐹 = |𝒜¬𝐹 |/(|𝒜¬𝐹 |+|𝒜¬𝐹 |) are coefficients to weight
𝑁 𝑁 𝐶 𝑁 𝑁 𝑁 𝐶
similarities proportionally to the size of the involved set of attributes, 𝜓 + = 𝑒𝑥𝑝(|𝒜𝐹 |/(|𝒜𝐹 | +
|𝒜¬𝐹 |) − 1) and 𝜓 − = 𝑒𝑥𝑝(|𝒜¬𝐹 |/(|𝒜𝐹 | + |𝒜¬𝐹 |) − 1) are smoothing factors to penalize
correlation-clustering weights that are computed on a small number of attributes (which is
usually the case for sensitive attributes, and hence negative-type weights), and 𝑠𝑖𝑚𝑆 (·) denotes
any object similarity function defined over the subspace 𝑆 of the attribute set.
Problem 2 (Attribute Selection for Fair Clustering). Given a set of objects 𝒳 over the at-
tribute sets 𝒜𝐹 , 𝒜¬𝐹 , find maximal subsets 𝑆 𝐹 ⊆ 𝒜𝐹 and 𝑆 ¬𝐹 ⊆ 𝒜¬𝐹 , with |𝑆 𝐹 | ≥ 1, |𝑆 ¬𝐹 | ≥
1, s.t. the weights computed by Eqs. (2)–(3) satisfy the global-weight-bounds condition in Th. 1.
Heuristics. Our first proposal to solve Problem 2 is a greedy heuristic, dubbed Greedy, which
iteratively removes the attribute that leads to the correlation-clustering weights with the lowest
target ratio until our global condition is satisfied. This algorithm runs in 𝒪(|𝒳 |2 |𝒜|2 ) time
�Table 2
Fair clustering results. Values correspond to averages over the dataset-specific statistics (values under the
column ‘orig.-weights Min-CC obj.’ were normalized for each dataset prior to the average calculation).
target %(𝑤+ orig.-weights avg. Eucl. avg. intra-clust intra-clust inter-clust inter-clust
#it time
ratio > 𝑤− ) Min-CC obj. fairness #clusts. 𝒜¬𝐹 𝒜𝐹 𝒜¬𝐹 𝒜𝐹 (seconds)
initial – 1.289 95.735 0.182 0.046 25.8 0.611 0.537 0.376 0.142 –
Hlv 19.75 0.96 88.19 0.435 0.054 4.5 0.461 0.231 0.377 0.145 481.281
Hlv_B 16.75 0.905 82.752 0.507 0.093 510.5 0.761 0.705 0.409 0.141 460.475
Hmv 11.25 0.981 96.630 0.124 0.032 22.3 0.556 0.383 0.311 0.139 387.605
Hmv_B 10.25 0.967 94.722 0.264 0.054 239.3 0.732 0.673 0.398 0.149 346.156
Hlv_BW 15.0 0.96 82.985 0.880 0.129 777.3 0.883 0.850 0.407 0.147 378.958
Hmv_SW 11.0 0.955 96.447 0.085 0.019 3.5 0.493 0.279 0.293 0.136 447.854
Greedy 7.75 0.966 95.558 0.105 0.037 15.0 0.581 0.507 0.381 0.145 3324.521
since, at each iteration, for each candidate attribute to be removed 𝒪(|𝒳 |2 ) similarities are
computed to quantify the decrease of the target ratio. We also devised other heuristics which,
like Greedy, remove one attribute at time, but exploit some easy-to-compute proxy measures to
select the attribute that avoid the pairwise similarity computation for each candidate attribute.
The Hlv (resp. Hmv) heuristic removes the least (resp. most) variable attribute where the
variability is measured through normalized entropy for categorical attributes and with variation
coefficient (capped to 1 if above 1) for numerical features. Hlv_B and Hmv_B, like the previous
two heuristics, remove the least and most variable attribute, respectively, but the selection is
constrained to the biggest set of features among 𝒜𝐹 and 𝒜¬𝐹 , in order to try to balance their
size. Hlv_BW removes the least variable attribute from the set (𝒜𝐹 or 𝒜¬𝐹 ) which induces
the highest average similarity value using the current weights, whereas Hmv_SW selects the
most variable attribute from the set which induces the lowest average similarity value using the
current weights. Note that all these heuristics (but Greedy) run in 𝒪(|𝒳 |2 |𝒜|) time.
Data and results. We considered 4 relational datasets: CreditCardCustomers, Adult, Bank, and
Student.4 For each of them, Table 1 shows the number of objects, a pair of values corresponding
to the count of non-sensitive and sensitive attributes, and a description of the latter.
Table 2 summarizes results achieved by each of the above heuristics, on the various datasets,
according to the following criteria (columns from left to right): number of iterations at con-
vergence, target ratio, percentage of pairs 𝑢, 𝑣 having 𝑤𝑢𝑣 + > 𝑤 − ; also, computed w.r.t. the
𝑢𝑣
full attribute space are: value of the objective function, average Euclidean fairness [9], average
number of clusters, intra-cluster and inter-cluster similarities according to either the subset
of sensitive attributes or the subset of non-sensitive attributes, and running time. Euclidean
and Jaccard similarity functions are used for numerical and categorical attributes, resp., and the
overall similarity is obtained by linear combination analogously to Eqs. (2)–(3). Note that higher
values correspond to better performance for 𝒜𝐹 -based intra-cluster and 𝒜¬𝐹 -based inter-cluster
similarities, while the opposite holds for the other two measures and the Euclidean fairness. The
first row in each table refers to the initial, full-attribute-space status of the relational network,
as a baseline, whereby the global-weight-bounds condition is not satisfied.
Hlv_BW and Hlv_B tend to produce solutions that correspond to the highest (i.e., worst)
value of the objective function and of the clustering size; this should be ascribed to the fact
4
https://www.kaggle.com/sakshigoyal7/credit-card-customers; https://archive.ics.uci.edu/ml/index.php
�that both heuristics favor the selection of the least variable attributes. By contrast, Hmv_SW is
the best performing in terms of objective function and Euclidean fairness. This method also
tends to produce very few clusters. Note that, while a higher number of clusters is found to be
coupled with a worsening of the objective function, the opposite does not hold in general; also,
in contrast to the intuition that a higher percentage of pairs having 𝑤+ > 𝑤− should favor the
grouping into fewer clusters, we observed that the clustering sizes are not necessarily ordered
as with the percentage ordering. As far as efficiency, Hmv_B mostly provides the best time
performance. While being one order of magnitude slower, Greedy tends to converge in less
iterations, as it indeed removes fewer attributes than the other methods; we found that in some
cases (e.g., Student, Adult, results not shown), this allows Greedy for compensating its expected
higher cost per iteration. Notably, each method lowers the initial target ratio below 1 so as to
satisfy the global condition, and the per-dataset best-performing method (not shown) improves
all intra-/inter-cluster similarities and Euclidean fairness w.r.t. the baseline.
6. Conclusions
We discussed a novel perspective in correlation clustering, which considers global weight
bounds. A sufficient condition is defined to extend the range of validity of approximation
guarantees beyond local weight bounds, such as the probability constraint. Experimental results,
including a case study in fair clustering, put in evidence of the usefulness of our approach.
One interesting future direction we plan to investigate is to define the edge weights (bounds)
based on relational properties of the objects under an uncertain data modeling framework [10].
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