=Paper= {{Paper |id=Vol-2123/paper19 |storemode=property |title=Sequential Pattern Mining using FCA and Pattern Structures for Analyzing Visitor Trajectories in a Museum |pdfUrl=https://ceur-ws.org/Vol-2123/paper19.pdf |volume=Vol-2123 |authors=Nyoman Juniarta,Miguel Couceiro,Amedeo Napoli,Chedy Raïssi |dblpUrl=https://dblp.org/rec/conf/cla/JuniartaCNR18 }} ==Sequential Pattern Mining using FCA and Pattern Structures for Analyzing Visitor Trajectories in a Museum== https://ceur-ws.org/Vol-2123/paper19.pdf

Sequential Pattern Mining using FCA and
Pattern Structures for Analyzing Visitor
Trajectories in a Museum

Nyoman Juniarta, Miguel Couceiro, Amedeo Napoli, and Chedy Raı̈ssi

Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
{nyoman.juniarta, miguel.couceiro, amedeo.napoli, chedy.raissi}@loria.fr

Abstract. This paper presents our work on mining visitor trajectories
in Hecht Museum (Haifa, Israel), within the framework of CrossCult Eu-
ropean Project about cultural heritage. We present a theoretical and
practical research work about the characterization of visitor trajectories
and the mining of these trajectories as sequences. The mining process
is based on two approaches in the framework of FCA, namely the min-
ing of subsequences without any constraint and the mining of frequent
contiguous subsequences. Both approaches are based on pattern struc-
tures. In parallel, a similarity measure allows us to build a hierarchical
classification which is used for interpretation and characterization of the
trajectories w.r.t. four well-known visiting styles.

Keywords: FCA, pattern structures, sequence clustering, sequential
pattern mining

1 Introduction

This paper is related to the CrossCult European Project about cultural heritage
(http://www.crosscult.eu/). The general idea of CrossCult is to support the
emergence of a European cultural heritage by allowing visitors in different loca-
tions (e.g. museum, city, archaeological site) to consider their visit at a European
level by using adapted computer-based devices.
In this project, we are mainly interested in the analysis of visitor trajecto-
ries and recommendation. The trajectory of a visitor in a specific location is
considered as a multi-dimensional sequence depending on a number of variables,
such as space (e.g. paths, rooms, environment), time (e.g. hour, day, season,
news), history and geography (e.g. town, region, country. . . ). Moreover, addi-
tional domain knowledge and general knowledge bases such as DBpedia, Free-
base or YAGO can be reused to draw inferences and improve the quality of both
analysis and recommendation.
Here, we have two main objectives, (i) the mining of visitor trajectories based
on sequence mining, and (ii) the characterization of a trajectory in terms of the
subsequences which are mined. We assume that the subsequences are related to
the visiting styles, the visit content, and the environment. Thus subsequences can

c paper author(s), 2018. Proceedings volume published and copyrighted by its editors.
Paper published in Dmitry I. Ignatov, Lhouari Nourine (Eds.): CLA 2018, pp.
231–242, Department of Computer Science, Palacký University Olomouc, 2018.
Copying permitted only for private and academic purposes.
232 Nyoman Juniarta, Miguel Couceiro, Amedeo Napoli, and Chedy Raı̈ssi

be used for analyzing the trajectory of a visitor and for making recommendations
all along the visit. Moreover, the occurrences of some subsequences at a given
moment within a trajectory can witness a change of behavior –which triggers in
turn a change in the recommendations.
In the present paper, we discuss theoretical and practical work about the def-
inition of visitor trajectories and the mining of these trajectories as sequences.
The mining process is based on two approaches about sequence mining in Formal
Concept Analysis (FCA [1]): MRGS for “Mining Rare General Subsequences” [2]
and MFCS for “Mining Frequent Contiguous Subsequences” [3]. The first approach
mines rare subsequences in a general way, i.e. gaps may appear in the subse-
quences, while the second approach searches for frequent subsequences without
any gap (a kind of substrings). If the original paper about MRGS [2] was inter-
ested in rare subsequences, this is no more the case here and we work on frequent
subsequences as well. We also reuse the similarity measure simACS developed
for analyzing the trajectories of patients between hospitals [4,5]. This similarity
measure allows us to build a hierarchical classification that will play a role of
“reference classification”. For analyzing and interpreting the trajectories of visi-
tors, it is interesting to compare the outputs of MRGS and MFCS algorithms w.r.t.
the clustering produced by simACS . Moreover, these outputs and the cluster-
ing as well are analyzed thanks to four theoretical visiting styles, namely “ant”,
“butterfly”, “fish” and “grasshopper” [6].
Several challenges are faced in this research work in the FCA framework:
the mining of complex sequential data and dynamics in adapting two algorithms
based on pattern structures, the analysis of the trajectories based on jumping
emerging patterns and clustering. Here, data are not necessarily big but are
rather complex and multidimensional and this should be taken into account.
The paper is organized as follows. Section 2 recalls the basic definitions about
sequence mining that are useful for understanding the present work. Then, Sect. 3
presents the characteristics of the dataset that was used as a basis for the cur-
rent work. In Sect. 5 and Sect. 6, we present first the application of clustering on
data enabling to build classes of visitors, and then the application of two algo-
rithms for mining interesting subsequences. Finally, in Sect. 7, an interpretation
of the results and a discussion on the characterization of the visitor trajectories
conclude the paper.

2 Sequence Mining
2.1 Basic Definitions
Pattern mining is the task of finding repeated occurrences in a dataset. For ex-
ample, in a dataset about customer transactions, an objective can be to find a
set of items that are frequently ordered in a single transaction. Another com-
plex objective is to detect a set of items that are likely ordered within certain
transactions. These specific tasks in pattern mining are related to sequential
pattern mining. We recall below the most important definitions that we need in
the following.
Sequential Pattern Mining using FCA for Analyzing Trajectories 233

Definition 1. A sequence is an ordered list hs1 s2 . . . sm i, where si is an itemset
{i1 , . . . , in }. Here, m is the
Psize of a sequence. The length of a sequence is the
total number of items, or |si |.

For example, h{a, b}{a, c, d}i is a sequence with size 2, since it contains two
itemsets. Its length is 5.

Definition 2. A sequence s = hs1 s2 . . . sm i is a subsequence of s0 = hs01 s02 . . . s0n i,
denoted by s s0 , if there exist indices 1 ≤ i1 < i2 < . . . < im ≤ n such that
sj ⊆ s0ij for all j = 1 . . . m and m ≤ n.

Therefore, the sequence h{a}{d}i is a subsequence of h{a, b}{a, c, d}i, while
sequence h{c}{d}i is not.
One way of evaluating the quality of a subsequence is to compute the sup-
port of the subsequence. Given a user-defined threshold, the subsequence can be
“frequent”, i.e. the support is above the threshold, or “rare”, i.e. the support is
below the threshold.

Definition 3. Let S be a sequential database. The support of a sequence s in
S is: support(s, S) = |{si ∈ S; s si }|

There exist algorithms which can retrieve all frequent sequences [7,8]. A long
sequence can have a combinatorial number of subsequences. Thus, if a long
sequence is frequent, these algorithms return all of its subsequences. This leads
to the retrieval of many uninteresting patterns. This issue has been studied
in [9,10,11] by introducing the concept of “closed sequence”. They narrow the
output by disregarding sequences which have another supersequence with the
same support (hence not closed).
Beside mining frequent sequences, another complex task is clustering. To
achieve such a task, a distance or a similarity measure between two sequences has
to be defined. The similarity measure simACS was proposed in [5], which counts
the number of all common subsequences (ACS). This measure is formulated as:

φC (Si , Sj )
simACS (Si , Sj ) = (1)
max{φD (Si ), φD (Sj )}
where φC (Si , Sj ) is the number of all common distinct subsequences between Si
and Sj , while φD (Si ) is the number of all distinct subsequences of Si .
In this paper, we reuse simACS with a restriction. Actually, we consider
sequences whose itemsets include only one item. For simplicity, we omit the
curly brackets to describe an itemset. Therefore we will write h{a}{d}{e}i as
ha, d, ei.

2.2 Sequence Mining in FCA

In this section we briefly present the two algorithms that are adapted for mining
the trajectories of visitors in a museum, namely MFCS [3] and MRGS [2]. The names
of the algorithms are not used as such in the papers but here we use them by
234 Nyoman Juniarta, Miguel Couceiro, Amedeo Napoli, and Chedy Raı̈ssi

commodity. Both algorithms are original and very efficient, and among the few
algorithms performing sequence mining in the framework of FCA.
MFCS was originally introduced for mining trajectories of patients in hospi-
tals. The algorithm is based on pattern structures and projections, and stability
as well. One important characteristic of MFCS is that it mines contiguous sub-
sequences, or stated differently, subsequences without any gap between items.
This is due to the fact that physicians are mainly interested in consecutive events
when analyzing healthcare trajectories. In addition, but this is not needed in our
framework, MFCS is able to take into account a partial ordering – given by domain
knowledge for example – defined on the items composing the sequences.
MRGS is also a sequence miner based on pattern structures but with a different
purpose. The objective of MRGS is to mine rare rather than frequent subsequences,
and in particular long subsequences with special characteristics. The algorithm
is based on a specific pattern structure of subsequences, where the similarity
operation is based on the discovery of common close subsequences (SCCS oper-
ation illustrated in a next section). The SCCS operation is based on a directed
graph of alignments (DAG of alignments) which guide the mining of common
subsequences. The algorithm shows very good performances and is most proba-
bly one of the few algorithms whose objective is the mining of rare subsequences.
In our framework, we adapted MRGS and the support threshold for comparison
purposes with frequent subsequences. However, we will use in our context MRGS
as a standard sequence miner and we will be interested in frequent subsequences.

3 The Dataset of Museum Visitors
3.1 The Museum
In the framework of the CrossCult project, we are working on a specific dataset
about the trajectories of 254 visitors in Hecht Museum in Haifa, Israel [12]. In
the raw dataset, a visitor trajectory contains a list of visited items, where each
visit is composed of three elements namely “start time”, “end time”, and “item
name”. An example is presented in Table 1.

Table 1: An example of one visitor trajectory
Start Finish Item
12:55:39 12:58:05 Crafts and Arts
12:58:06 12:58:22 Religion and Cult
12:58:22 12:58:27 Building Methods and Facilities
12:58:29 13:05:09 Wooden Tools

A visitor can have visits with various time lengths. In order to obtain more
meaningful results and to reduce the complexity, we only consider visits last-
ing at least 90 seconds, but this is a parameter that can be relaxed or more
Sequential Pattern Mining using FCA for Analyzing Trajectories 235

constrained. Thirty-eight trajectories have no visit more than this threshold,
so they are ignored, leaving us with 216 trajectories. Moreover, we model each
trajectory as a sequence of visited items. Therefore, for trajectory in Table 1,
the corresponding sequence is hCrafts and Arts, Wooden Toolsi. This prepro-
cessing results in sequences of various size. Forty-five sequences have only one
itemset, while three sequences have more than 15 itemsets.

Table 2: Grouping of museum items
Category Items and their ID
1 Entrance Reuben Hecht (101), Symbols Jewish Menorah (102),
Persian Cult (103), Jerusalem Photo (104)
··· ···
8 Upper Floor Entrance (801), Coins (802), Seven Species (803),
Oil Lamps (804), Weights (805), Temple Mount (806),
Jerusalem (807), Greece Egypt (808), Cyprus (809), Gems (810)

We group the museum items according to their location, so that we obtain
8 categories of items. Some of them are listed in Table 2. We convert the raw
dataset into sequences of items, where each item is represented by its ID. We
define the IDs such that we can infer the category of an item by its first digit.
Therefore, we obtain a dataset of 216 sequences of visitor trajectories – named
V1 –V216 – where each sequence is composed by a list of IDs, as illustrated in
Table 3.

Table 3: Examples of visitor trajectories
Visitor Trajectory
V1 h101, 101, 401, 704i
V2 h102, 402, 808, 206, 808i
V3 h302, 102, 201, 302, 705, 402, 802i
V4 h104, 704, 602, 302, 402, 103i

3.2 The Four Visiting Styles
In a seminal work about the typing of visitor styles in a museum [6], four main
behaviors have been detected and described, leading to different recommenda-
tions all along a visit [13,14]. These four styles are summarized below:

– The ant denotes a visitor who will surely see all the works following their
location order in the museum. Then the recommendation can be the following
236 Nyoman Juniarta, Miguel Couceiro, Amedeo Napoli, and Chedy Raı̈ssi

item, but depending also on some environmental factors such as the crowd
in the museum, the accessibility of the item and the fatigue of the visitor.
– The grasshopper denotes a visitor who will see only certain artworks, jumping
from one to the other. Then, to encourage such a person to visit more items,
the recommendation can be to visit items having a content similar to items
already visited.
– The butterfly denotes a visitor wanting to discover some and not all artworks,
without having any exact preferences. Then, the recommendation is open
and can be based on surprise (items which are very different one from the
other).
– The fish denotes a visitor who does not feel that much interested in the
artworks and stays most of the time in the center of the rooms without any
precise objective. Then the recommendation can be to visit the most famous
items in the museum which are the closer to the current visitor location, for
encouraging the visitor to continue the visit and gain more interest.

Indeed, a visitor can change his/her style during a visit and other elements
may be of importance, e.g. crowd or fatigue of the visitor.

4 The Workflow for Analyzing the Trajectories

In the following, one objective is to map specific subsequences included in the
visitor trajectories to each visiting style for characterizing more precisely the
style and then making smarter recommendations. To identify the behavior of
each visitor, we propose the following workflow:

1. Cluster the visitor trajectories and attach a label for each visitor (Sect. 5).
2. Create two concept lattices using MFCS and MRGS over the whole dataset
(Sect. 6.1).
3. From the two lattices, find jumping emerging patterns (JEPs) for each label
(Sect. 7.2).
4. Based on their JEPs, these labels are then mapped into four visiting styles
that has been explained in Sect. 3.2.

5 The Clustering of Trajectories

In this first experiment, we reuse the simACS similarity measure for clustering
the visitor trajectories. The idea is to check whether it is possible to distinguish
the four visiting styles introduced in Section 3.2. We applied hierarchical cluster-
ing1 based on simACS to build a distance matrix between individuals. From the
resulting dendrogram, we retained 5 clusters denoted by “A”, “B”, “C”, “D”,
and “E”. Four of them are expected to match the four visiting patterns, namely
ant, butterfly, fish, and grasshopper. The last cluster will gather all non-classified
1
We used the hclust method from the R software [15].
Sequential Pattern Mining using FCA for Analyzing Trajectories 237

trajectories. These five clusters have various sizes. Cluster “A”, “B”, “C”, “D”,
and “E” have 11, 11, 59, 102, and 33 visitors respectively.
Actually, it is not easy to directly match the five clusters to corresponding vis-
iting styles. For doing so, we will analyze the subsequences that can be attached
to each cluster of trajectories. The benefit of the clustering is actually to provide
a label among “A”, “B”, “C”, “D”, and “E” to the visitors. Thanks to these
labels, we can perform a search for the so-called “jumping emerging patterns”
and attach a characterization to the clusters based on the mined subsequences.

6 The Mining of Trajectories Considered as Sequences
6.1 Mining Subsequences with MFCS and MRGS
Below, we explain the application of the MFCS and MRGS algorithms to the mu-
seum dataset and the building of an associated concept lattice. Moreover, as
will be discussed in the next section, the jumping sequential patterns which are
mined will help us to characterize the visitor trajectories.
In MFCS and MRGS, pattern structures are used for mining sequences. The
similarity operator (u) between any two sets of sequences is defined as the set
of closed common subsequences (SCCS) in the two input sequences. Then, given
two sequences, say S1 = h401,502,503i and S2 = h401,503,502i, the similarity
between these descriptions is:

δ(S1 ) u δ(S2 ) = {h401,502,503i} u {h401,503,502i}
= {h401,502i, h401,503i}

In the dataset, the items are grouped into categories and the SCCS calcu-
lation is performed, checking whether two items belong to the same category.
Using the MFCS algorithm [3] it becomes:

δ(S1 ) u δ(S2 ) = {h401,502,503i} u {h401,503,502i}
= {h502i, h503i, h401,5,5i}

It should be noticed that MFCS mines contiguous subsequences, i.e. in Defini-
tion 2, ik = ik−1 + 1 for all k ∈ {2, 3, . . . , m}.
In parallel, the default similarity operator of MRGS can be modified to ac-
commodate our needs, such that non-contiguous common subsequences can be
mined:

δ(S1 ) u δ(S2 ) = {h401,502,503i} u {h401,503,502i}
= {h401,502i, h401,503i, h401,5,5i}

Then, based either on MFCS or MRGS, a concept has an extent including a set
of trajectories and an intent including a set of common subsequences. Again, it
238 Nyoman Juniarta, Miguel Couceiro, Amedeo Napoli, and Chedy Raı̈ssi

should be noticed that, based on whether a subsequence is contiguous or not,
the resulting concept lattices are different.
For example, the concepts corresponding to Table 3 are shown in Table 4.
Notice that both algorithms obtain a concept whose extent is V2 , V3 , V4 , albeit
with different intent. Based on MRGS, the common subsequence of V2 , V3 , V4 is
h1, 402i, while according to MFCS, their common subsequences are h1i and h402i.
This is because items 1 and 402 are not contiguous in V3 and V4 .

Table 4: The concepts that are computed by of MFCS and MRGS from four visitors
in Table 3
Extent Intent (MFCS) Intent (MRGS)
V1 h101,101,401,704i
V2 h102,402,808,206,808i
V3 h302,102,201,302,705,402,802i
V4 h104,704,602,302,402,103i
V1,2 h1,4i not present
V1,4 h1i, h4i, h704i h1,1i, h1,4i, h1,704i
V2,3 h2i, h102i, h402,8i h102,402,8i, h102,2,8i
V3,4 h1i, h302i, h402i, h7i h1,302,402i, h302,1i, h1,7,402i
V1,3,4 h1i, h4i, h7i h1,4i, h1,7i
V2,3,4 h1i, h402i h1,402i
V1,2,3,4 h1i, h4i h1,4i

6.2 Jumping Emerging Patterns
FCA is a non supervised classification process that can be turned into a super-
vised process thanks to the adding of a target attribute in the context, generally
corresponding to a target class. Then the idea is to search for the so-called
“Jumping Emerging Patterns” (JEPs) [16]. We have already applied this ap-
proach in [17] for analyzing and characterizing clusters of biological inhibitors.
Here we adapt the same idea for characterizing this time the clusters of visitors
discovered with the similarity measure simACS .
More precisely, five clusters are discovered by classifying visitor trajectories
with simACS . These same trajectories are then considered as sequences com-
posed of subsequences. Then a set of characteristic subsequences is extracted
and these subsequences are used as “attributes” in a formal context where ob-
jects are visitor trajectories. The resulting formal context is completed with an
extra attribute corresponding to the “cluster information”, i.e. the cluster in
which the trajectory is classified according to simACS . A concept lattice can
then be built from this completed context.
More interestingly, the cluster information is used for characterizing the con-
cepts whose extents include trajectories of a single cluster. The intents – made
of subsequences – of these particular concepts are JEPs, and as such they can
Sequential Pattern Mining using FCA for Analyzing Trajectories 239

be used to characterize the corresponding clusters. For example, if the extent
of the concept ({V103 , V165 , V188 }, {h4i, h1i, h306i, h701,707i}) includes visitors
from cluster B only, then its intent is JEPs for that cluster.

7 Discussion

7.1 About Interesting Subsequences

The first part of Table 5 shows some interesting contiguous subsequences from
4677 concepts discovered by MFCS. Thirty-three persons are visiting three items
contiguously in category 1 of items located near the entrance. This is interesting
to be noticed, as visitors are likely to spend more time in rooms located near the
entrance, because they are arriving, they have high interest, and they are not
tired. Then items of importance could be placed near the entrance for getting
sufficient interest from visitors.
Thirteen people visit an item in category 7 – this category corresponds to
items in the room of “Ancient Ship” which is one of the most famous items
in this museum – right after an item in category 1. This is a characteristic of
grasshopper, because 1 and 7 are separated by many other categories. These
visitors have a specific interest for the “Ancient Ship” in the museum, since they
skip all the items located between entrance and “Ancient Ship” (both categories
can be considered as “far” from each others).
From 8019 concepts obtained by MRGS, some subsequences are presented in
the second part of Table 5. The subsequence h1,1i has a support of 69 with
MFCS, and it has quite a similar support (66) with MRGS. Then we can draw the
same conclusion, meaning that when a person visits two items in category 1, it is
likely in continuation (to be compared with the preceding subsequence h1,1,1i).
Now, more interestingly, there are 38 persons visiting an item in category 3
after category 1, while much less persons (9) are doing the opposite. A similar
conclusion can also be drawn with pairs h4,7i (31) and h7,4i (11). Based on such
an observation, we can infer that visiting a museum is an “oriented activity”and
that some directions are more preferred than others or “naturally followed”,
just as it is the case for the ordering of the rooms existing in the museum. By
contrast, only a few visitors are quitting the “natural flow” and go “backward”.
Among these visitors, we can probably find visitors searching for more precision
about preceding visited items.

7.2 Cluster Characterization

Now we are interested in characterizing the five clusters that were introduced in
the previous section. For doing so, JEPs are searched in the two concept lattices
obtained with MFCS and MRGS algorithms. Some of these concepts are listed in
Table 6 and Table 7.
First, from both MFCS and MRGS, we cannot find any satisfying concept for
JEP of cluster “E”. This is because among all the concepts whose extent is
240 Nyoman Juniarta, Miguel Couceiro, Amedeo Napoli, and Chedy Raı̈ssi

Table 5: Some interesting subsequences mined by MFCS (left) and MRGS (right)
Subsequence Support
Subsequence Support
h1,3i 38
h1,1,1i 33 h3,1i 9
h1,7i 13 h4,7i 31
h1,1i 66 h7,4i 11
h1,1i 69

exclusively from cluster “E”, none of them has more than one visitor. If we
consider the dataset, among 33 members of cluster “E”, 32 of them visit less
than 2 items during their whole visit. We can assume that they are visitors that
are not really interested in visiting the museum. Therefore, we can quote safely
label this cluster as fish.
Cluster “D” is more easily distinguishable. Based on subsequences of concept
FD2–FD4, many visitors in this class skip some items. Also, in concept RD1 and
RD2, some of them visit other items after item 701. This is not a natural direction,
because items in category 7 are located farther from the entrance than items in
category 4 or 5. We can interpret the visitors of this cluster as grasshopper, since
they “jump” from one item to another.
Clusters “A”, “B”, and “C” are relatively similar to each other. The visitors
associated to these clusters follow an ant behavior: a natural flow (based on RA1–
RC1) and no “jump” (based on FA1–FC2). However, in FC3, three visitors visit
101, then 102, then back again to 101, indicating rather a butterfly behavior.

Table 6: Interesting concepts discovered by the MFCS algorithm
Concept ID Extent Intent Support Cluster
FA1 {V70 , V107 , V121 , V133 , V201 , V202 } {h1,1,402i, h103i, h2i} 6 A
FA2 {V70 , V93 , V107 , V121 } {h402i, h103,104i} 4 A
FB1 {V103 , V165 , V188 } {h4i, h1i, h306i, h701,707i} 3 B
FC1 {V4 , V8 , V28 , V32 , V84 , V152 } {h102i, h101,1,101i} 6 C
FC2 {V53 , V152 , V169 , V189 , V190 , V203 } {h7i, h102,4i} 6 C
FC3 {V4 , V8 , V32 } {h101,102,101i} 3 C
FD1 {V54 , V105 , V139 , V168 } {h202,4i} 4 D
FD2 {V139 , V168 } {h202,405,701i} 2 D
FD3 {V46 , V47 } {h101,602i} 2 D
FD4 {V89 , V163 } {h602,203i} 2 D

7.3 Conclusion
In this article, we have presented our experiments in mining visitor trajecto-
ries that are modeled as sequences of items. We incorporated a classification of
Sequential Pattern Mining using FCA for Analyzing Trajectories 241

Table 7: Interesting concepts discovered by the MRGS algorithm
Concept ID Extent Intent Support Cluster
RA1 {V70 , V107 , V121 , V133 , {h1,1,402,2i, h1,1,4i, 6 A
V201 , V202 } h103,402,2i, h103,4i}
RB1 {V142 , V183 , V192 } {h102,1,1,1,1i, h102,103,1,1i, 3 B
h1,1,1,1,1i, h1,103,1,1i}
RC1 {V4 , V8 , V28 , V84 , V152 } {h1,1,1,101i, h1,101,1,101i, 5 C
h1,1,1,1i, h1,101,1,1i,
h101,1,1,1i, h101,101,1,1i,
h101,101,101i, h102,101i, h102,1i}
RD1 {V71 , V79 } {h701,504i} 2 D
RD2 {V97 , V98 } {h701,406i} 2 D

museum items and built a concept lattice using pattern structures. We applied
two sequence miners based on FCA to the visitor trajectories, namely MFCS and
MRGS, to discover interesting contiguous and general subsequences.
Our result highlight some interesting patterns that may define visitor behav-
iors. This can help museum researchers to analyze and evaluate the placement
of items and the visiting styles. Moreover, we have also studied the possibility of
clustering the visitors based on a concept lattice. These clusters can be analyzed
to build a recommendation system for future visitors, but we did not yet study
this aspect until now.
In this paper, we only included in the sequences partial information about
the museum. More interesting results can be obtained if other elements are taken
into account, such as more general knowledge about history and geography, and
duration and time of the visit. . . Furthermore, the selection of interesting con-
cepts can be also guided by computing the stability of the concepts [18]. Finally,
from a more dynamic point of view, ongoing information such as comments and
state of the visitor during the visit could be also considered for analysis and
in-line recommendation.

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