Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization

Abstract

There are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.

Keywords

Fuzzy c-means clustering intuitionistic fuzzy sets process mining customer behaviors indoor locations

Get full access to this article

View all access options for this article.

References

Dogan

, Bayo-Monton

J.-L.

, Fernandez-Llatas

and Oztaysi

, Analyzing of gender behaviors from paths using process mining: A shopping mall application, Sensors 19(3) (2019), 557.

Dogan and

Oztaysi , Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN, Expert Systems with Applications 136 (2019), 42–49.

Dogan and

Oztaysi , In-store behavioral analytics technology selection using fuzzy decision making, Journal of Enterprise Information Management 31(4) (2018), 612–630.

De Leoni ,

W.M.

van der Aalst and

Dees , A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs, Information Systems 56 (2016), 235–257.

Arroyo ,

J.J.

Yebes ,

L.M.

Bergasa ,

I.G.

Daza and

Almazán , Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls, Expert systems with Applications 42(21) (2015), 7991–8005.

Yoshimura ,

Sobolevsky ,

Ratti ,

Girardin ,

J.P.

Carrascal ,

Blat and

Sinatra , An analysis of visitors’ behavior in the louvre museum: A study using bluetooth data, Environment and Planning B: Planning and Design 41(6) (2014), 1113–1131.

Hwang and

Y.J.

Jang , Process mining to discover shoppers-pathways at a fashion retail store using a wifi-base indoor positioning system, IEEE Transactions on Automation Science and Engineering 14(4) (2017), 1786–1792.

Abedi ,

Bhaskar ,

Chung and

Miska , Assessment of antenna characteristic effects on pedestrian and cyclists traveltime estimation based on bluetooth and wifi mac addresses, Transportation Research Part C, Emerging Technologies 60 (2015), 124–141.

Mou ,

D.J.

Robb and

DeHoratius , Retail store operations: Literature review and research directions, European Journal of Operational Research 265(2) (2017), 399–422.

10.

Fernandez-Llatas ,

Lizondo ,

Monton ,

J.-M.

Benedi and

Traver , Process mining methodology for health process tracking using real-time indoor location systems, Sensors 15(12) (2015), 29821–29840.

11.

W.M.

van der Aalst , Process mining: Data science in action, Springer, 2016.

12.

R.J.C.

Bose and

W.M.

Van der Aalst , Context aware trace clustering: Towards improving process mining results, in: Proceedings of the 2009 SIAM International Conference on Data Mining, SIAM, 2009, pp. 401–412.

13.

Song ,

C.W.

Günther and

W.M.

Van der Aalst , Trace clustering in process mining, in: International Conference on Business Process Management, Springer, 2008, pp. 109–120.

14.

Ferreira ,

Zacarias ,

Malheiros and

Ferreira , Approaching process mining with sequence clustering: Experiments and findings, in: International Conference on Business Process Management, Springer, 2007, pp. 360–374.

15.

Hompes ,

J.C.

Buijs ,

W.M.

van der Aalst ,

Dixit and

Buurman , Detecting Change in Processes Using Comparative Trace Clustering, in: SIMPDA, 2015, pp. 95–108.

16.

Dogan , Heuristic Approaches in Clustering Problems, in: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, IGI Global, 2018, pp. 107–124.

17.

Kahraman ,

Oztaysi ,

S.C.

Onar and

Dogan , Intuitionistic fuzzy originated type-2 fuzzy ahp: An application to damless hydroelectric power plants, International Journal of the Analytic Hierarchy Process 10(2) (2018), 266–292.

18.

Huang and

Ng , A fuzzy k-modes algorithm for clustering categorical data, IEEE Transactions on Fuzzy Systems 7(4) (1999), 446–452.

19.

K.T.

Atanassov , Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1) (1986), 87–96.

20.

á.

Rebuge and

D.R.

Ferreira , Business process analysis in healthcare environments: A methodology based on process mining, Information systems 37(2) (2012), 99–116.

21.

Frisby ,

Smith ,

Traub and

V.L.

Patel , Contextual computing: A bluetooth based approach for tracking healthcare providers in the emergency room, Journal of Biomedical Informatics 65 (2017), 97–104.

22.

M.C.

Popa ,

L.J.

Rothkrantz ,

Shan ,

Gritti and

Wiggers , Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information, Pattern Recognition Letters 34(7) (2013), 809–819.

23.

M.L.

van Eck ,

Sidorova and

W.M.

van der Aalst , Enabling process mining on sensor data from smart products, in: Research Challenges in Information Science (RCIS), 2016 IEEE Tenth International Conference on, IEEE, 2016, pp. 1–12.

24.

Kang and

Hansen , Behavioral analysis of airline scheduled block time adjustment, Transportation Research Part E, Logistics and Transportation Review 103 (2017), 56–68.

25.

Delafontaine ,

Versichele ,

Neutens and

Van deWeghe , Analysing spatiotemporal sequences in bluetooth tracking data, Applied Geography 34 (2012), 659–668.

26.

Y.-K.

Wu ,

H.-C.

Wang ,

L.-C.

Chang and

S.-C.

Chou , Customer’s flow analysis in physical retail store, Procedia Manufacturing 3 (2015), 3506–3513.

27.

Yim ,

Jeong ,

Gwon and

Joo , Improvement of kalman filters for wlan based indoor tracking, Expert Systems with Applications 37(1) (2010), 426–433.

28.

Oosterlinck ,

D.F.

Benoit ,

Baecke and

Van de Weghe , Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits, Applied Geography 78 (2017), 55–65.

29.

Chen ,

Fern and

Todorovic , Multi-object tracking via constrained sequential labeling, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1130–1137.

30.

Dogan ,

O.F.

Gurcan ,

Oztaysi and

Gokdere , Analysis of Frequent Visitor Patterns in a Shopping Mall, in: Industrial Engineering in the Big Data Era, Springer, 2019, pp. 217–227.

31.

A.A.

Shaw and

Gopalan , Finding frequent trajectories by clustering and sequential pattern mining, Journal of Traffic and Transportation Engineering 1(6) (2014), 393–403.

32.

Marchetti ,

Zhou , et al., Solution path clustering with adaptive concave penalty, Electronic Journal of Statistics 8(1) (2014), 1569–1603.

33.

Evermann ,

Thaler and

Fettke , Clustering traces using sequence alignment, in: International Conference on Business Process Management, Springer, 2016, pp. 179–190.

34.

D’ Urso and

Massari , Fuzzy clustering of human activity patterns, Fuzzy Sets and Systems 215 (2013), 29–54.

35.

Jiang ,

Ferreira and

M.C.

González , Clustering daily patterns of human activities in the city, Data Mining and Knowledge Discovery 25(3) (2012), 478–510.

36.

Manzi ,

Dario and

Cavallo , A human activity recognition system based on dynamic clustering of skeleton data, Sensors 17(5) (2017), 1100.

37.

Fernández-Llatas ,

J.-M.

Benedi ,

J.M.

García-Gómez and

Traver , Process mining for individualized behavior modeling using wireless tracking in nursing homes, Sensors 13(11) (2013), 15434–15451.

38.

W.M.

van der Aalst , How people really (like to) work, in: International Conference on Human-Centred Software Engineering, Springer, 2014, pp. 317–321.

39.

Zadeh , Fuzzy sets, Information and Control 8(3) (1965), 338–353.

40.

Szmidt and

Kacprzyk , Distances between intuitionistic fuzzy sets, Fuzzy Sets and Systems 114(3) (2000), 505–518.

41.

Xie and

Beni , A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8) (1991), 841–847.

42.

R.R.

Yager , On some new classes of implication operators and their role in approximate reasoning, Information Sciences 167(1-4) (2004), 193–216.

43.

J.C.

Dunn , A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics 3(3) (1973), 32–57.

44.

J.C.

Bezdek , Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 1981.

45.

Oztaysi and

Isik , Supplier Evaluation Using Fuzzy Clustering, in: Supply Chain Management Under Fuzziness, Springer Berlin Heidelberg, 2014, pp. 61–79.

46.

Chaira , A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images, Applied Soft Computing 11(2) (2011), 1711–1717.