what are potential issues with these conclusions: This research uses data mining technologies to discover five association rules of customer value for air travellers. The

study uses Ward$$s method to partition air travel market of Taiwan into four markets via shopping benefit variables. The

study uses Ward$$s method to partition air travel market of Taiwan to be four markets via shopping benefit variables.

The FSLC model $($RFM model based, Frequency, high Season travelling, Locations, Cancellation$)$ is a proposed model

for discovering customer value for airlines of Taiwan, the Frequency, high Season travelling, and Locations are usually

positive profit variables for airlines; the Cancellation variable is a negative profit variable $($Chiang and Liang $2004)$ for

airlines. The research applies the FSLC model and socio$-$economic variable on the Supervised Apriori Algorithm to

extract five useful association rules.

The objective of this study is to discover customer knowledge, such as finding the optimised tourist market targets

for airlines in Taiwan. To implement their marketing projects, the airlines can filter travellers from the membership database to find optimised target market via the rules. For example, to apply rule $4,$ in this rule, the travellers are, females,

from $31$ to $40,$ with HHHH of customer value, belonging to market C $($convenience dimension$),$ and usually go travelling individually or with family. In this case, the airlines can implement marketing projects on the filtered target travellers, and the project features are $$high season$,$Locations: America, Europe, or Africa$,$ as well as with convenience

dimension $($Market C$,$ such as arrival airport locations, number of flights, and internet order easily$),$ the projects may

attract the travellers of rule $4.$

For applications of rule $2,$ airlines can filter the membership database with rule $2$ to find the target travellers who

are male, from $21$ to $30,$ and with LHLH of customer value. These travellers belong to market B $($cost dimension$)$ and

usually go travelling individually or with family. In this case, the airlines can implement marketing projects on the filtered target travellers, and the project features are $$high season$,$Locations: Asia, Australia$/$New Zealand$,$ as well as

with cost dimension $($Market B$,$ such as FFP$,$ credit card feedback, and ticket price$),$ the projects may attract the travellers of rule $2.$

However, the regular airlines in Taiwan can apply these rules on their tourist markets. To apply the strategic plans

by using the data mining technologies in the membership database of marketing or CRM system is an appropriate marketing method for airlines in Taiwan. The procedure of this research can be applied in other industries $($such as logistics

industry$)$ for discovering optimised target markets as well.