Function-based Case Classification for Improving Business Process
Mining
Yaguang Sun and Bernhard Bauer
Programming Distributed Systems Lab, University of Augsburg, Augsburg, Germany
Keywords:
Business Process Mining, Multi-label Case Classification, Sequential Pattern Mining, Business Process
Extension.
Abstract:
In the last years business process mining has become a wide research area. However, existing process mining
techniques encounter challenges while dealing with event logs stemming from highly flexible environments
because such logs contain a large amount of different behaviors. As a result, inaccurate and wrong analysis
results might be obtained. In this paper we propose a case (a case is an instance of the business process)
classification technique which is able to combine domain experts knowledge for classifying cases so that
each group is calculated containing the cases with similar behaviors. By applying existing process mining
techniques on the cases for each group, more meaningful and accurate analysis results can be obtained.
1 INTRODUCTION
Business process mining techniques aim at analysing
various aspects of enterprise business processes such
as workflow discovery, process performance analysis
and organizational structure analysis, etc. The starting
point of these analyses is usually an event log which
is a set of cases, where each case is an instance of the
business process (van der Aalst, 2011). Cases always
have an attribute trace which is a finite sequence of
ordered events. In an event log, events and cases are
represented by unique identifiers (event ID and case
ID). Except for trace, a case may also have other at-
tributes such as originator and cost, etc. An event can
also have its own attributes. Table 1 shows an exam-
ple event log.
In the real world business processes are often ex-
ecuted in highly flexible environments, e.g., health-
care, customer relationship management (CRM) and
product development (Weerdt et al., 2013). The event
logs that stem from such flexible environments (real-
life event logs) often contain a high variety of case be-
haviors. The behaviors of a certain case are reflected
by the values of case attributes. For instance, the be-
havior of a case related to the case attribute trace is
called structural case behavior which is expressed by
the events and their precedence relations in the trace.
Cases are generated so as to adhere to a certain cat-
egory of behaviors determined by business process-
based domain criterion (Weerdt et al., 2013). The pro-
Table 1: An example event log.
Case id Event id Properties
Activity Resource Cost
1 421 A Pete 40
422 B Sun 200
423 C Simon 300
424 D Chris 100
425 E Pete 200
2 452 A Mike 30
453 C Simon 300
454 F Chris 200
455 D Sun 100
456 G Mike 500
. . . . . . . . . . . . . . .
cess mining techniques are facing difficulties while
dealing with event logs containing multiple process-
based criteria. For instance, inaccurate and complex
business process models might be generated by exist-
ing workflow discovery algorithms with an input of
such logs because multiple criteria may represent a
lot of structural behaviors of cases from structurally
different process executions. Furthermore, some im-
portant process analysis results related to certain pro-
cess criterion might be concealed in the final results
generated by using the logs containing multiple pro-
cess criteria. There is a need to classify the cases with
different behaviors into different groups.
Accordingly, some approaches are developed for
251
Sun Y. and Bauer B..
Function-based Case Classification for Improving Business Process Mining.
DOI: 10.5220/0005349202510258
In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 251-258
ISBN: 978-989-758-096-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Model of case classification in the scenario of process mining.
this purpose. One efficient technique is trace clus-
tering (Greco et al., 2006; Song et al., 2009; Bose
and van der Aalst, 2010; Bose and van der Aalst,
2009) which is designed mainly to help discover bet-
ter workflows. It is able to automatically capture the
behaviors of cases in an event log and then group the
cases with similar behaviors into the same sub-log.
By applying process mining techniques on each sim-
pler sub-log more accurate and understandable analy-
sis results can be obtained.
Trace clustering can help find a lot of hidden be-
haviors among the cases. However, it is an unsu-
pervised learning technique and lack domain knowl-
edge. As a result it is unable to indicate which be-
haviors found are crucial for classifying the cases or
which behaviors are wanted by customers for classi-
fying the cases. Additionally, treating all of the be-
haviors found equally may not generate a correct or
meaningful classification of cases.
Classification (supervised learning) which is able
to combine the domain knowledge from business ex-
perts can be a useful tool for classifying cases. In this
paper we put forward a case classification technique:
• We demonstrate and formalise the problem of
multi-label case classification in Section 3.1.
• One important sub-problem of the case classifica-
tion problem is how to exploit the case attribute
trace for classifying the cases. To solve this sub-
problem we develop a systematic method based
on sequential pattern mining technique for utilis-
ing the case attribute trace for classifying cases in
Section 3.2 and Section 3.3.
• To test the efficiency of our method, we make a
case study in Section 4 by using a real-life event
log of a Dutch academic hospital from Business
Process Intelligence Contest 2011 (BPIC 2011).
2 PROBLEM DESCRIPTION
Figure 1 depicts a detailed model for case classifica-
tion in the scenario of business process mining. Ba-
sically, the case-classifier links the primitive cases in
an event log with the labels. Each label represents one
category and cases connected to the same label may
share some common behaviors. Finally by working
with the already classified cases, the process mining
techniques are able to analyse the enterprise business
process in different points of view and generate more
readable and meaningful analysis results.
Traditional data classification approaches proceed
in two steps (Kotsiantis, 2007):
– In the first step, training data is analysed by a
classification algorithm so that a function y =
f (X) can be learned. The generated function
(classifier) is able to predict the associated cate-
gory label y of a given tuple X = (x
1
, x
2
, ..., x
n
),
where X represents the set of attributes of a spe-
cific item.
– In the second step, predictive accuracy of the
classifier built is estimated by utilising a test set
made up of tuples and their relevant category la-
bels.
A training event log should be generated firstly
for a case classification problem. Labels are added
manually to part of the cases in an event log by do-
main experts according to their domain knowledge
and the behaviors of cases. Then these labeled cases
are extracted to generate the training event log and
test event log. A training event log is used for build-
ing the classifier and a test event log for estimating the
performance of the classifier. By looking into some
real-life event logs we discover that a case may have
more than one label which makes this case classifica-
ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems
252
tion problem a multi-label classification problem (as
shown in Figure 1).
The behaviors of cases are reflected by the val-
ues of case attributes recorded in the event log. Most
case attributes that have discrete values or numeric
values can be easily utilised for building the classi-
fier or judging which labels a case belongs to. But the
trace of a case can not be directly used. A trace is an
important attribute of a case which is a finite sequence
of ordered events, for instance, in Table 1 the trace of
case1 is < A,B,C, D,E >. The trace may be a major
element for deciding which labels a case pertains to
(while the labels are related to the structural feature
of trace).
For the cases generated by structured business
processes
1
, it is easy to transform the traces into a
suitable form that can be utilised as an attribute for
case classification because the compositions of traces
are limited by a structured process model. However,
for the cases from a real-life event log, such a trans-
formation should not be directly carried out because it
is possible that cases with similar features may seem
very different from each other. Additionally, there
might be multiple structural features (presence or ab-
sence of an activity, presence or absence of combina-
tions of activities and so on) of traces related to one
label. So how to capture the possible structural be-
haviors of traces (expressed by the events and their
precedence relations in the traces) relevant to labels is
an important sub-problem for solving the multi-label
case classification problem.
To solve the multi-label case classification prob-
lem and its sub-problem mentioned above, we pro-
posed in this paper a sequential pattern mining
technique-based method (Section 3) for mining all of
the possible label-related structural features of traces
and then transforming these found features into suit-
able forms of case attributes to help the later case clas-
sification.
3 BASIC CONCEPTS AND
APPROACH DESIGN
In this section we first elaborate the concept of multi-
label case classification (Section 3.1). Afterwards the
structural feature of traces is presented in a formal
way (Section 3.2). At last we provide a method for
1
A structured business process is a rigidly defined pro-
cess with a model which considers all of the process in-
stance permutations and every process instance complies
with this model. If a structured business process has a loop
structure it can also generate a large amount of isomerous
traces.
transforming the found structural features of traces
into a suitable form of case attributes for the later case
classification (Section 3.3).
3.1 Multi-label Case Classification
A multi-label classification technique solves the prob-
lem of predicting to which set of classes (also rep-
resented by labels) a new instance belongs by ex-
ploiting a training set of data. The training data is
a set T = {t
1
, t
2
, ..., t
n
} of already classified sam-
ples where each sample t
i
is constructed by a k-
dimensional vector X
t
i
= (x
i
1
, x
i
2
, ..., x
i
k
). The di-
mensions in X
t
i
represent attributes of the sample, as
well as the categories to which t
i
pertains.
The existing multi-label classification methods are
mainly divided into two types, one is algorithm in-
dependent and the other one is algorithm dependent
(Tsoumakas and Katakis, 2007; Carvalho and Freitas,
2009). In this paper we will utilise the algorithm in-
dependent approach for solving the problem of multi-
label case classification. In the algorithm independent
approach, a multi-label classification problem can be
converted into several single-label problems. For each
label (or category) a classifier is built so that the clas-
sification problem related to this label can be dealt
with. For the multi-label case classification problem
in this paper, the training data is conveyed by the fol-
lowing definitions:
Definition 1. Let C
t
be the set of training cases. A
case c ∈ C
t
is defined as a tuple c = (N
c
, L
c
, Θ
c
),
where N
c
= {n
1
, n
2
, ..., n
k
} is the set of names of
case attributes, L
c
is a set of labels, Θ
c
: N
c
→ A
c
is
an attribute-transition function which maps the name
of an attribute into the value of this attribute, where A
c
is the set of attribute values for case c. A label l ∈ L
c
represents a manually given class to which case c be-
longs.
As already mentioned, a case in an event log may
be assigned multiple labels in the real world, thus for
all c ∈ C
t
, we have |L
c
| ≥ 1, where |L
c
| is the number
of labels.
Definition 2. A training event log is defined as E
t
⊆
C
t
, for any c
1
,c
2
∈ E
t
such that c
1
6= c
2
.
Let’s presume that the example event log in Ta-
ble 1 is a training event log, all cases in this log have
an attribute originator and an attribute labels, case1
has an originator ”Mike” and a set of assigned labels
{l
1
,l
2
}. According to the concepts defined above,
Θ
1
(originator) = ”Mike” is the originator for case1,
Θ
1
(trace) =< A,B,C,D,E > is the trace for case1,
L
1
= {l
1
,l
2
} is the set of labels for case1.
Function-basedCaseClassificationforImprovingBusinessProcessMining
253
Definition 3. The multi-label case classification
problem is defined as Prob = (SE
t
, SE
test
, Φ, Ξ),
where SE
t
is the set of training event logs, SE
test
represents the set of test event logs for evaluating
the accuracy of the learned classifiers, Φ is a multi-
label classification algorithm, Ξ is a classifier eval-
uation schema. Φ : SE
t
→ Ψ represents the pro-
cess for building a classifier (or a set of classifiers),
where Ψ is the set of classifiers. Ξ : SE
test
,Ψ →
{Accurate,Inaccurate} represents the process for
evaluating the performance of a built classifier.
3.2 Definitions Relevant to Function
The sequential pattern mining techniques solve the
problem of finding all frequent subsequences from a
given set of sequences, where each sequence contains
a list of ordered events and each event consists of a
set of items (Han and Kamber, 2000). A minimum
support threshold is manually given for judging if the
occurrence of a subsequence is frequent or not.
Let I = {I
1
, I
2
, ..., I
n
} be the set of all items,
S = {S
1
, S
2
, ..., S
m
} be the set of all sequences. A
sequence S
i
∈ S is an ordered list of events and de-
noted < e
i1
, e
i2
, ..., e
i j
>, where each event e
ik
rep-
resents an item set composed by items of I. For
any two sequences α =< a
1
, a
2
, ..., a
l
> and β =<
b
1
, b
2
, ..., b
q
> over S, α is a subsequence of β, if
1 ≤ p
1
< p
2
< · · · < p
l
≤ q such that a
1
= b
p
1
, a
2
=
b
p
2
, ..., a
l
= b
p
l
. Let D ⊆ S be a database of se-
quences, for a given minimum support threshold
min sup (0 < min sup < 1), a sequence λ is called
a sequential pattern if support(λ) ≥ min sup × |D|,
where support(λ) is the number of sequences in D
which contain λ and |D| represents the total number
of sequences in D.
As introduced in Section 3.1, the trace of a case is
a sequence of ordered events. Thus the set of traces
collected from an event log can be deemed as a se-
quence database on which the sequential pattern min-
ing algorithms can be implemented. We define a se-
quential pattern mined from a set of traces extracted
from an event log as a function:
Definition 4. Let E be an event log, Y ⊆ E be a set of
traces collected from E, Γ : Y
min sup
−→ F be a sequential
pattern mining algorithm, where F = { f
1
, f
2
, ..., f
n
}
is the mined set of sequential patterns with a mini-
mum support threshold min sup, a sequential pattern
f
i
∈ F is defined as a function relevant to Y and F is
also called a set of functions.
Definition 5. Let E
t
be a training event log, Y
label
⊆
E
t
be a set of traces from E
t
, where each trace in Y
label
is related to one common label. A label-related func-
tion set F
label
is a set of functions mined from Y
label
,
Set of traces Set of traces Set of traces - - - - - -
Training event
log
Label-related
functions
Sequential
pattern mining
Label n
Label 1
Label m
1
m n
Set of functions
Figure 2: Mining label-related functions from a training
event log.
and a function f
k
∈ F
label
is called a label-related func-
tion.
In our opinion, the label-related functions in F
label
reveal the commonly and frequently appeared struc-
tures of the traces in Y
label
. As mentioned in Section 2,
a label may be associated with the structural charac-
teristics of traces. While this is true, the label-related
functions can be exploited to judge if a trace belongs
to a specific label.
Figure 2 illustrates the process of mining all of the
possible label-related functions from a training event
log. In the first step, all the traces included by a train-
ing event log are separated into different sets where
each set is associated with one label. For example,
in Figure 2 the traces of cases with label 1 are col-
lected and sent to set 1. In the second step, a sequen-
tial pattern mining procedure is executed on each set
of traces for discovering label-related functions. Fi-
nally all of the found functions are grouped together
in one set (label-related function set).
Let’s presume that the event log shown in Table 1
is a training event log and each case in this log has a
set of labels from {l
1
,l
2
,l
3
,l
4
}. For this training event
log, a set of traces Y
l
2
is obtained through extracting
all traces with label l
2
. Then by mining Y
l
2
using a se-
quential pattern mining method a label-related func-
tion set F
l
2
can be extracted. This label-related func-
tion mining procedure mentioned above is described
in Algorithm 1.
3.3 Transforming Label-related
Functions into Case Attributes
The raw label-related functions found could not be
exploited directly, they should be transformed into a
suitable form of case attributes so that they can be
ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems
254
Algorithm 1 Mining label-related functions for a
given training event log
Input: a training event log E
t
, the minimum support
threshold min sup
Let G(label) be a set of traces related to a specific
label in E
t
.
Let S be a set of all possible label-related func-
tions for E
t
.
Let LB be the set of labels in E
t
.
Let Θ be a case attribute-transition function as de-
scribed in Definition 1.
Let Γ(Y,m s) → F be a sequential pattern mining
algorithm, where Y is a sequence database, m s is
a minimum support threshold, F is the mined set
of sequential patterns.
1: S ←
/
0 // is set to be an empty set
2: for each G(label
i
) such that label
i
∈ LB do
3: G(label
i
) ←
/
0
4: end for
5: for each case c
j
∈ E
t
do
6: for each label l
k
∈ L
c
j
do
7: G(l
k
) = G(l
k
) ∪ Θ
c
j
(trace) // L
c
j
is the
set of labels owned by c
j
8: end for
9: end for
10: for each label lb ∈ LB do
11: S = S ∪ Γ(G(lb),min sup)
12: end for
Output: the set of all label-related functions in E
t
: S
checked by a classification algorithm. In the follow-
ing parts we will propose a method for this purpose.
Through algorithm 1, the traces in a training
event log are grouped into different sets where
each set is related to one label and the functions
for each set are discovered. Before building a
classifier for each label, the found functions need
to be converted into usable case attributes. To
explain our method clearly, we will first set up a
few variants: E
t
is a training event log, a set G =
{G(label
1
), G(label
2
), ..., G(label
n
)} where each
G(label
i
) is a set of all the traces relevant to label
i
in
E
t
, a set F = {F(label
1
), F(label
2
), ..., F(label
n
)}
where each F(label
j
) is a function set
for G(label
j
) found by algorithm 1, F
∗
=
{ f unction
1
, f unction
2
, ..., f unction
m
} is a set
which contains all of the functions in F. An asso-
ciation table AT is then established which connects
each function in F
∗
with a global unique identifier. In
the association table shown in Table 2, for instance,
f unction
1
has an id A
1
.
Table 2: An example association table for the functions in
F
∗
.
Function ID Function
A
1
f unction
1
A
2
f unction
2
.
.
.
.
.
.
A
n
f unction
n
Let c
p
∈ E
t
be a case from training event log E
t
,
add all of the function ids in the association table as
attribute names to the attribute list of c
p
, and their ini-
tial values are set to 0. The next step is to calculate the
value of each new added attribute. Take function A
1
from Table 2 as an example, a subsequence-detection
process is carried out with Θ
c
p
(trace) (the trace of
c
p
) and f unction
1
(matched with A
1
in Table 2) as in-
puts. If f unction
1
is judged to be a subsequence of
Θ
c
p
(trace), then Θ
c
p
(A
1
) is reset to be 1. The proce-
dure mentioned above should also be applied to both
the test event logs and the normal event logs which
contain cases needed to be classified.
4 CASE STUDY
We tested the effectiveness of our technique on the
hospital event log from the BPIC 2011. This log con-
tains 624 activities and 1143 cases where each case
stands for a treatment process of a patient of the gy-
naecology department. A lot of attributes relevant to
the cases have been recorded in this log, such as the
ages of patients and the final diagnosis for patients.
In the experiment the treatments performed on a
patient are regarded as labels (categories). One rea-
son is that in healthcare industry treatment is often
used as a label for classifying cases, for instance, the
SAP Business Suite for Patient Management exploits
the treatment as one category for case classification
(SAP Community, 2014). There are overall 48 kinds
of treatment (coded by number) in this log from which
we have chosen seven frequently happened treatments
(namely 13, 23, 61, 101, 113, 603, 3101) for analysis.
Each case may belong to more than one treatments
and each treatment may be characterized by multiple
behaviors of traces.
For generating the training event log, a simple
strategy mentioned in (Tsoumakas and Katakis, 2007)
is exploited which discards every multi-label case in
the hospital event log. For instance, all the cases that
have only a single treatment belonging to the treat-
ment set TS = {13, 23, 61, 101, 113, 603, 3101} are
extracted and form the training event log. All of the
Function-basedCaseClassificationforImprovingBusinessProcessMining
255
Table 3: Performances of the classifiers built for each treatment in TS.
Treatment Correctly classified AUC Kappa statistic Recall
instances ratio
13 0.896940 0.935 0.6662 0.988/0.882
23 0.900161 0.850 0.6060 0.782/0.917
61 0.901771 0.968 0.7241 0.729/0.959
101 0.597424 0.679 0.2935 0.481/1.000
113 0.887279 0.740 0.0590 0.068/0.973
603 0.855072 0.758 0.3304 0.638/0.873
3101 0.853462 0.873 0.5335 0.900/0.847
multi-label cases are organized as the test event log.
As a result, we obtain a training event log with 279
cases and a test event log with 621 cases.
In Algorithm 1 the closed sequential pattern min-
ing algorithm
2
BIDE (Wang and Han 2004) is used.
By implementing Algorithm 1 with the training event
log and a min sup = 0.3 as input parameters, a label-
related function set S which contains 3159 functions
has been discovered. Then all of the found functions
are transformed into case attributes for both the train-
ing event log and the test event log through method
introduced in Section 3.3. In the generated associ-
ation table AT, the id of a function is in the form
of FPattern
k
where k represents the position of this
function in AT. For example, A
1
= FPattern 1 in Ta-
ble 2 because f unction
1
is the first item and A
n
=
FPattern n because f unction
n
is the n
th
item in this
association table.
In this paper we utilise an algorithm independent
approach for solving the problem of multi-label case
classification which converts the learning problem
into traditional single-label classification. For each
element in TS a classifier is learned by using a train-
ing event log. For example, for treatment 13, a clas-
sifier is built which is able to judge if a case falls in
treatment 13 or not. In our experiment seven binary
classifiers (because seven kinds of treatment are anal-
ysed) are established.
To testify the standpoint that the labels pertained
by a case may be related to the structural feature of
its trace, only the case attributes generated by the
transformation of discovered label-related functions
are considered in our experiment, for instance, in this
hospital event log, the other case attributes such like
Age and Diagnisis are not put to use.
Firstly we use the Decision Tree-based algorithm
2
The reason to use a closed sequential pattern mining
algorithm is that it effectively decreases the total number of
sequential patterns generated but in the meantime preserves
the complete information about all the sequential patterns.
FPattern
1086
113
1
FPattern
1047
FPattern
1
not 113
1
113
0
1
not 113
0
0
(a) Decision Tree for treatment 113
FPattern
3127
3101
1
FPattern
3128
3101
1
not 3101
0
0
(b) Decision Tree for treatment 3101
Figure 3: Decision Trees built for treatment 113 and treat-
ment 3101.
C4.5 (Quinlan, 1993) in our experiment. For each
treatment in TS a decision tree is built by exploit-
ing the attributes of cases. For example, we got a
decision tree for treatment 113 as shown in Figure
3(a), and Figure 3(b) shows the decision tree for treat-
ment 3101. According to the decision tree for treat-
ment 113, a case will be inferred to belong to treat-
ment 113 if it has an attribute FPattern 1086 = 1,
and not if it has the attributes FPattern 1086 = 0 and
FPattern 1047 = 0 at the same time.
Table 3 shows the performances for the seven clas-
sifiers evaluated by using the test event log gener-
ated. Several parameters are calculated in the eval-
uation step. The area under the ROC curve (AUC)
which has a value between 0 and 1 reflects the per-
formance of the classification model. An ideal classi-
fier has an AUC value close to 1. Correctly Classified
ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems
256
13 23 61 101 113 603 3101
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Labels
Value of ICS−Fitness
Test−Fitness
Predicted−Fitness
Original−Fitness
Figure 4: ICS-Fitness for the models generated by using the sub-logs from SC and PC.
Instances Ratio reflects the total classification accu-
racy of a specific classifier. The Kappa Statistic (KS)
measures the diversity factor between the classifica-
tion results from a classifier built and a classification
by chance, KS ∈ (0.75, 1) implies that the effect of
the classifier is very good, KS ∈ (0.4, 0.75) is char-
acterized as fair to good and KS ∈ (0, 0.4) as poor.
Recall measures the proportion of correctly classified
instances among all the instances with the same label
or without a specific label.
Additionally, to testify the practicability of our
technique in business process mining area, we then
evaluate the effectiveness of the classification re-
sults (obtained by using Decision Tree algorithm) on
the process discovery task (the most crucial learn-
ing task in process mining domain). Let C
test
be
the test event log generated in our experiment, SC =
{C
13
, C
23
, C
61
, C
101
, C
113
, C
603
, C
3101
} be the set
of sub-logs where each sub-log contains the cases
correlates to one treatment from T S. Let PC =
{PC
13
, PC
23
, PC
61
, PC
101
, PC
113
, PC
603
, PC
3101
}
be the set of sub-logs where each consists of the
cases predicted to have one same treatment from T S.
Then process models for the entire test log C
test
and
for each sub-log in SC and PC are learned by us-
ing process discovery techniques. In our experiment
Heuristic Mining Algorithm as described in (Weijters
and Ribeiro, 2011) is utilised. Afterwards, the ICS-
Fitness (Weerdt et al., 2013) (fitness measures the
proportion of behavior in the event log possible ac-
cording to the model) for each model is calculated
and the results are shown in Figure 4. The ICS-
Fitness related to each sub-log in SC is called Test-
Fitness and the ICS-Fitness for the sub-logs in PC is
called Predicted-Fitness. The Original-Fitness (as a
base line in Figure 4) is calculated by using the en-
tire test event log C
test
and the model for it. In Figure
4 we can see that the Predicted-Fitnesses and Test-
Fitnesses for most of the sub-logs in SC and PC are
much higher than the Original-Fitness due to the sub-
logs in SC and PC are simpler and contain less behav-
iors than the original event log C
test
, as a result the
models generated for these sub-logs become more ac-
curate. By comparing the Predicted-Fitness with the
Test-Fitness in Figure 4, we discovered that most of
the Predicted-Fitnesses are very close to their relevant
Test-Fitnesses, for instance, the value of Predicted-
Fitness for PC
3101
is 0.9267 and the value of Test-
Fitness for C
3101
is 0.9382 which is very close to
the Predicted-Fitness for PC
3101
. From the analy-
ses mentioned above it can be deduced that the re-
sults from the multi-label case classification tech-
nique have some practical values in the business pro-
cess mining area. Process discovery is only one per-
spective of business process mining techniques and
the multi-label case classification method can also
benefit other techniques in this area (e.g., business
process performance analysis) to help generate more
meaningful and accurate analysis results.
5 RELATED WORK
In the literature, different approaches have been put
forward to overcome the negative impacts from high
variety of behaviors stored in event logs on business
process mining techniques. One efficient technique is
trace clustering.
In (Song et al., 2009) the authors present an ap-
proach for characterizing the traces by profiles for the
later trace clustering. Each profile is a set of items
that describe the trace from a specific angle. Five
profiles are defined in (Song et al., 2009), they are
activity profile, transition profile, case attributes pro-
Function-basedCaseClassificationforImprovingBusinessProcessMining
257
file, event attributes profile and performance profile.
Then by converting the profiles defined into an aggre-
gate vector the distance between any two traces can
be calculated. One advantage of this technique is that
it provides a full range of metrics for clustering traces.
Context-aware trace clustering methods are pro-
posed in (Bose and van der Aalst, 2010) and (Bose
and van der Aalst, 2009). In (Bose and van der Aalst,
2010) the authors indicate that the feature sets based
on sub-sequences of different lengths are context-
aware for the vector space model and can reveal some
set of common functionality accessed by the process.
Two traces that have a lot of common conserved fea-
tures should be put in the same cluster. In (Bose
and van der Aalst, 2009) the authors presents an edit
distance-based approach for partitioning traces into
clusters such that each cluster consists of traces with
similar structure. The cost of edit operations is asso-
ciated with the contexts of activities so that the calcu-
lated edit distance between traces is more accurate.
In (Weerdt et al., 2013) a novel technique for trace
clustering is presented which is able to directly opti-
mise the accuracy of each cluster’s underlying pro-
cess model. This method doesn’t consider the vector
space model or define a metric for trace clustering, it
simply discovers the suitable traces for each cluster
so that the combined accuracy of the related models
for these clusters is maximized. This method suffi-
ciently resolves the divergence between the clustering
bias and the evaluation bias.
Classification technique is widely used on De-
cision Mining area in business process mining. In
(Rozinat and van der Aalst, 2006) the author devel-
oped a Decision Miner based on Decision Tree algo-
rithm which aims at analysing the choice constructs
of process models by exploiting the event attributes
recorded in event logs.
6 CONCLUSION
In this paper we proposed and elaborated the basic
definition of Multi-label Case Classification. Next,
a concrete systematic method was introduced which
is able to discover all of the label-related structural
features of traces and transform these found features
into case attributes for the later classification job. The
effectiveness and practicability of our technique were
then testified through a case study.
Our next research task will be to focus on exploit-
ing the decision trees generated by our technique so as
to clearly reveal the influences of different categories
on the execution of business processes.
REFERENCES
Bose, R. and van der Aalst, W. (2009). Context Aware Trace
Clustering: Towards Improving Process Mining Re-
sults. In Proceedings of the SIAM International Con-
ference on Data Mining, pages 401–412.
Bose, R. and van der Aalst, W. (2010). Trace Cluster-
ing Based on Conserved Patterns: Towards Achiev-
ing Better Process Models. In Business Process
Management Workshops, volume 43 of Lecture Notes
in Business Information Processing, pages 170–181.
Springer Berlin.
SAP Community. (2014). Customer-defined Case Classifi-
cation. http://help.sap.com.
Greco, G., Guzzo, A., Pontieri, L., and Sacca, D. (2006).
Discovering Expressive Process Models by Cluster-
ing Log Traces. IEEE Transaction on Knowledge and
Data Engineering, 18(8):1010–1027.
Han, J. and Kamber, M. (2000). Data Mining: Concepts
and Techniques. Morgan Kaufmann, 2nd edition.
Kotsiantis, S. (2007). Supervised Machine Learning: A
Review of Classification Techniques. In Proceed-
ings of the 2007 Conference on Emerging Artificial
Intelligence Applications in Computer Engineering:
Real Word AI Systems with Applications in eHealth,
HCI, Information Retrieval and Pervasive Technolo-
gies, pages 3–24. IOS Press.
Quinlan, J. (1993). C4.5: Programs for Machine Learning.
Morgan Kaufmann.
Rozinat, A. and van der Aalst, W. (2006). Decision Min-
ing in Prom. In International Conference on Business
Process Management (BPM 2006), volume 4102 of
Lecture Notes in Computer Science, pages 420–425.
Springer Berlin.
Song, M., Gnther, C., and van der Aalst, W. (2009). Trace
Clustering in Process Mining. In Business Process
Management Workshops, volume 17 of Lecture Notes
in Business Information Processing, pages 109–120.
Springer Berlin.
Tsoumakas, G. and Katakis, I. (2007). Multi-label Classi-
fication: An Overview. International Journal of Data
Warehousing and Mining, 3(3):10–13.
Carvalho, A. and Freitas, A. A. (2009). A Tutorial on Multi-
label Classification Techniques. Foundations of Com-
putational Intelligence, Studies in Computational In-
telligence, pages 117-195. Springer Berlin.
van der Aalst, W. (2011). Process Mining: Discovery, Con-
formance and Enhancement of Business Processes.
Springer Berlin Heidelberg, Berlin, 1nd edition.
Weerdt, J. D., vanden Broucke, S., Vanthienen, J., and Bae-
sens, B. (2013). Active Trace Clustering for Improved
Process Discovery. IEEE Transaction on Knowledge
and Data Engineering, 25(12):2708–2720.
Weijters, A. and Ribeiro, J. (2011). Flexible Heuristics
Miner (FHM). In Proceedings of CIDM, pages 310–
317.
Wang, J. and Han, J. (2004). BIDE: Efficient Mining of Fre-
quent Closed Sequences. In 20th Int. Conf. on Data
Engineering, Boston, MA.
ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems
258
