Data mining for analysing kiosk usage behavior patterns

This main purpose of this study is to understand the self-seeking behavior of participants in developed kiosks that provides interactive service at Huashan Creative Park in Taipei City. To understand user self-seeking patterns, log data from actual cases of interactive kiosk service were collected and analysed by web usage mining. This study analysed 5724 sessions of 8 kiosks for the month of December, 2013. Sequential profiles for user self-seeking behavior patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. Self-seeking use cycle, time, function numbers, and the depth and extent (range) of services were statistically analysed. The analysis results can be used improved the public's acceptance of interactive kiosks and help generate potential recommendations for achieving an intelligent kiosk service.