Abstract TV shopping becomes far much popular in recent years. TV nowadays is almost everywhere. People watch TV; meanwhile, they are more and more accustomed to buy goods via TV shopping channel. Even in recession, it is thriving and has become one of the most important consumption modes. This study uses cluster analysis to identify the profiles of TV shopping consumers. The rules between TV Shopping spokespersons and commodities from consumers are recognized by using association analysis. Depicting the marketing knowledge map of spokespersons, the best endorsement portfolio is found out to make recommendations. By the analysis of spokespersons, period, customer profiles and products, fourbusiness modes of TV shopping are proposed for consumers: new product, knowledge, low price and luxury product; the related recommendations are also provided for the industry reference.

References

[1] Adderley R., Townsley M., and Bond J., Use of Data Mining Techniques To Model Crime Scene Investigator Performance, Knowledge-Based Systems, vol. 20, no. 2, pp. 170-176, 2007.

[2] Agrawal R., Imielinski T., and Swami A., Mining Association Rules Between Sets Of Items in Large Database, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, pp. 207-216, 1993.

[3] Agrawal R. and Shafer J., Parallel Mining of Association Rules, IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 962-969, 1996.

[4] Bhattacharyya S., Jha S., Tharakunnel K., and Westland J., Data Mining for Credit Card Fraud: A Comparative Study, Decision Support Systems, vol. 50, no. 3, pp. 602-613, 2011.

[5] Daneshvary R. and Schwer R., The Association Endorsement and Consumers' Intention to Purchase, Journal of Consumer Marketing, vol. 17, no. 3, pp. 203-213, 2000.

[6] Dudek D., RMAIN: Association Rules Maintenance Without Reruns Through Data, Information Sciences, vol. 179, no. 24, pp. 4123-4139, 2009.

[7] Engel J., Warshaw M., Kinnear T., and Reece B., Promotional Strategy: an Integrated Marketing Communication Approach, Pinnaflex Educational Resources, 2000.

[8] Grant A., Guthrie K., and Ball-Rokeach S., Television Shopping: A Media System Dependency Perspective, Communication Research, vol. 18, no. 6, pp. 773-798, 1991.

[9] Han J., Data Mining: Concepts and Techniques, Elsevier Science Ltd, 2011.

[10] Hu Y. and Chen Y., Mining Association Rules with Multiple Minimum Supports: A New Mining Algorithm and A Support Tuning Mechanism, Decision Support Systems, vol. 42, no. 1, pp. 1-24, 2006.

[11] Johnson K., Yoo J., Rhee J., Lennon S., Jasper C., and Damhorst M., Multi-Channel Shopping: Channel Use Among Rural Consumers, International Journal of Retail and Distribution Management, vol. 34, no. 6, pp. 453-466, 2006.

[12] Kalra A. and Goodstein R., The Impact of Advertising Positioning Strategies on Consumer Price Sensitivity, Journal of Marketing Research, vol. 35, no. 2, pp. 210-224, 1998.

[13] Kar B. and Wu E., Authentication of Real-Time Communication System Using KIS Scheme, in Proceedings of Advanced Technologies, Embedded and Multimedia for Human-centric Computing, Dordrecht, pp. 1237-1246, 2014.

[14] Kouris I., Makris C., and Tsakalidis A., Using Information Retrieval Techniques for Supporting Data Mining, Data and Knowledge Engineering, vol. 52, no. 3, pp. 353-383, 2005.

[15] Lennon S., Sanik M., and Stanforth N., Motivations for Television Shopping: Clothing Purchase Frequency and Personal Characteristics, Clothing and Textiles Research Journal, vol. 21, no. 2, pp. 63-74, 2003.

[16] Li Y. and Shiu Y., A Diffusion Mechanism for Social Advertising Over Microblogs, Decision Support Systems, vol. 54, no. 1, pp. 9-22, 2012.

[17] Liao S., Chem Y., Liu F., and Liao W., Information Technology and Relationship Management: A Case Study of Taiwan's Small Manufacturing Firm, Technovation, vol. 24, no. 2, pp. 97-108, 2004.

[18] Liao S., Chen C., Hsieh C., and Hsiao S., Mining Information Users' Knowledge for One-to-One Marketing on Information Appliance, Expert Systems with Applications, vol. 36, no. 3, pp. 4967-4979, 2009.

[19] Liao S. and Wen C., Data Mining Theories and Applications: in Case Of IBM SPSS Modeler, DrMaster Press, 2012.

[20] Liao S., Wen C., Hsian P., Li C., and Hsu C., Mining Customer Knowledge for a Recommendation System in Convenience Stores, International Journal of Data Warehousing and Mining, vol. 10, no. 2, pp. 55-86, 2014.

[21] Lim C. and Kim Y., Older Consumers' TV Home Shopping: Loneliness, Parasocial Interaction, and Perceived Convenience, Psychology and Marketing, vol. 28, no. 8, pp. 763-780, 2011.

[22] Sharma A., The Persuasive Effect of Salesperson Credibility: Conceptual and Empirical Examination, Journal of Personal Selling and Sales Management, vol. 10, no. 4, pp. 71-80, 1990.

[23] Stephens D., Hill R., and Bergman K., Enhancing the Consumer-Product Relationship: Lessons From The QVC Home Shopping Channel, Journal of Business Research, vol. 37, no. 3, pp. 193-200, 1996.

[24] Ture M., Kurt I., Turhan Kurum A., and Ozdamar K., Comparing Classification Techniques for Predicting Essential Hypertension, Expert Systems with Applications, vol. 29, no. 3, pp. 583-588, 2005.

[25] Vrahatis M., and Boutsinas B., Alevizos P., and Pavlides G., The New K-Windows Algorithm for Improving Thek-Means Clustering Algorithm, Journal of Complexity, vol. 18, pp. 375-391, 2002.

[26] Wang Y., Chuang Y., Hsu M., and Keh H., A Personalized Recommender System for The Cosmetic Business, Expert Systems with Applications, vol. 26, no. 3, pp. 427-434, 2004. Mining Consumer Knowledge from Shopping Experience: TV Shopping Industry 1051

[27] Wen C., Liao S., Chang W., and Hsu P., Mining Shopping Behavior in the Taiwan Luxury Products Market, Expert Systems with Applications, vol. 39, no. 12, pp. 11257-11268, 2012.

[28] Yuvaraj D. and Hariharan S., Content-Based Image Retrieval Based on Integrating Region Segmentation and Colour Histogram, The International Arab Journal of Information Technology, vol. 13, no. 1A, pp. 203- 207, 2016.

[29] Zaafouri A., Sayadi M., and Fnaiech F., A Vision Approach for Expiry Date Recognition using Stretched Gabor Features, The International Arab Journal of Information Technology, vol. 12, no. 5, pp. 448- 455, 2015. Chih-HaoWen is an Assistant Professor of Department of Logistics Management at the National Defense University. His papers were published in various journals such as: Expert Systems With Applications, Maritime Economics and Logistics, IEEE Transactions on Systems, Man, and Cybernetics, Lecture Notes in Computer Science, International Journal of Data Warehousing and Mining, Journal of National Defense Management, National Defense Journal etc. His current other research interests include data mining, recommendation system, business intelligent, internet of things, information management, database managementand general management. Shu-Hsien Liao is a Professor in the Department of Management Sciences and Decision Making, Tamkang University, Taiwan. His research interests include decision theory, information management, knowledge management, database management, human resource management, technology management, marketing management and general management. His papers were published in various journals such as: European Journal of Operational Research, Journal of the Operational Research Society, Expert Systems With Applications, Technovation, Knowledge Management Research and Practices, Asia Paci c Management Review, Pan-paci c Management Review, Journal of Human Resource Management etc. Shu-Fang Huang is a Master of Business Administration. She was graduated from the Department of Management Sciences and Decision Making, Tam-Kang University, Taiwan. Her research interests include data mining, consumer knowledge extraction and customer shopping behavior.