EXPLORING THE EYE TRACKING DATA OF HUMAN BEHAVIOUR ON CONSUMER MERCHANDISE PRODUCT
This article presents an exploration of the human eye tracking data towards consumer products. The study aim to investigate the data attributes of the cognitive processes and focused on the visual attention of the participants when choosing a shampoo brand which is commonly available in Malaysia. However, eye tracking datasets has a wealth of data on the eyes visual attention, fixation, saccade and scan path gaze. Therefore, this paper aims to solve this problem to minimize the datasets by using clustering machine learning approach. This is to observe the relation of these data attributes and possibly predict the possible solution contributing to cognitive processing. Tobii TX300 Eye-tracker was used in this experiment and the eyes tracking data were gathered particularly related to the eyes fixation and saccades by using the Tobii I-VT filter. Sixty subjects participated in this study. K-means clustering was used as statistical analysis to cluster the huge datasets from the eye tracking data. The relationship of the consumer cognitive processes with visual attention was understood when most of the participants chose the most popular shampoo brand such as Head & Shoulder. Further visual analysis on the data attributes results showed that K-means clustering has the potential to cluster and minimize the huge datasets and predicts consumer preferences.
 M. Raschke, T. Blascheck and M. Burch, “Visual Analysis of Eye Tracking Data,” in Handbook of Human Centric Visualization, W. Huang, Ed. Switzerland AG: Springer, 2014, pp. 391-410.
 A. De Mauro, M. Greco, and M. Grimaldi, “What is big data? A consensual definition and a review of key research topics,” AIP Conference Proceedings, vol. 1644, no. 1, pp. 97-104, 2015.
 I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The rise of ‘big data’ on cloud computing: Review and open research issues”, Information Systems, vol. 47, pp. 98-115, 2015.
 R. L. Fantz, "Visual experience in infants: Decreased attention to familiar patterns relative to novel ones", Science, vol. 146, no. 3644, pp. 668-670, 1964.
 R. Pieters and L. Warlop, “Visual attention during brand choice: The impact of time pressure and task motivation”, International Journal of Research in Marketing, vol. 16, no. 1, pp. 1-16, 1999.
 S. Goyal, K. P. Miyapuram and U. Lahiri, “Predicting Consumer’s Behavior Using Eye Tracking Data,” in Second International Conference on Soft Computing and Machine Intelligence, Hong Kong, 2015, pp. 126-129.
 Q. Wang, M. Wedel, L. Huang and X. Liu, “Effects of model eye gaze direction on consumer visual processing: Evidence from China and America”, Information and Management, vol. 55, no. 5, pp. 588-597, 2018.
 K. Gidlof, A. Anikin, M. Lingonblad, and A. Wallin, “Looking is buying. How visual attention and choice are affected by consumer preferences and properties of the supermarket shelf”, Appetite, vol. 116, pp. 29-38, 2017.
 R. V. Menon, V. Sirgurdsson, N. M. Larsen, A. Fagerstrom, and G. R. Foxall , “Consumer Attention to price in social commerce: Eye Tracking Patterns in Retail Clothing”, Journal of Business Research, vol. 69, no. 11, pp. 5008-5013, 2016.
 M. Charrad, N. Ghazzali, V. Boiteau, and A. Niknafs, “NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set”, Journal of Statistical Software, vol. 6, no. 61, pp.1-36, 2014.
 T. Von der Malsburg and S. Vasishth, “What is the scanpath signature of syntactic reanalysis?”, Journal of Memory and Language, vol. 65, no. 2, pp. 109-127, 2011.
 I. S. Dhillon, Y. Guan and B. Kulis, "Kernel k-means: spectral clustering and normalized cuts", in Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, USA, 2004, pp. 551-556.
 I. S. Dhillon and D. S. Modha, “Concept Decompositions for Large Sparse Text Data using Clustering”, Machine Learning, vol. 42, no. 1-2, pp. 143-175, 2001.
 J. Bezdek, Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press, 1981.
 A. McCallum, K. Nigam and L. H. Ungar, "Efficient clustering of high-dimensional data sets with application to reference matching", in Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, USA, 2000, pp. 169-178, 2000.
 T. S. Madhulatha, “An Overview on Clustering Methods”, IOSR Journal of Engineering, vol. 2, no. 4, pp. 719-725, 2012.
 A. Mishra and O. Dutt, “Importance of Brand Name in Consumer Decision Making Process”, International Research Journal of Management Sociology and Humanity, vol.5, no. 2, pp. 873-890, 2014.
 H. H. Chovanova, A. I. Korshunov and D. Babcanova, “Impact of Brand on Consumer Behavior”, Procedia Economics and Finance, vol. 34, pp. 615-621, 2015.
 H. Zamani, A. Abas and M. K. M. Amin, “Eye Tracking Application on Emotion Analysis for Marketing Strategy”, Journal of Telecommunication, Electronics and Computer Engineering, vol. 8, no.11, pp.87-91, 2018.
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