Consumers are adopting different search strategies, so identifying the patterns of their information search habits has become a challenge. The way in which allocation of resources is done across different sources of information depends on the understanding of the patterns of information search behavior. Cluster analysis was used to identify the distinct segments of new car buyers concurrently, and the relative importance of important variables in differentiating the segments . With the help of classification it can predict categorical class labels. Different patterns of information search behavior were obtained across four different groups -broad temperate searchers, extreme heavy searchers, low broad searchers, and low searchers. Here both cluster analysis and classification was compared to identify consumer assortment of external pre-purchase information search behavior and the most optimized method found, out of the two was clustering.

1. F. O. Isinkaye, Y. O. Folojimi, B. A. Ojokoh. Recommendation Systems: Principles, Methods an evaluation,Volume 16, Issue 3, pp: 261–273, 2015
2. VikramShinde,Analysis of Research in Consumer Behavior of Automobile Passenger Car Customer. International Journal of Scientific and Research Publications, Volume 4, Issue 2, 2014.
3. L. V. Bijuraj. Clustering and its Applications. Proceedings of National Conference on New Horizons in IT-NCNHIT 2013.
4. Liu Dongyan, Bai Xuan. Car Purchasing Behavior in Beijing: An Empirical Investigation. Umea School of business and economics, University of Umea, Master Thesis, Spring Semester, 2008.
5. Ying Liua,b,* Jiajun Yanga. Improving Ranking-based Recommendation by Social Information and Negative Similarity,Volume 55, pp: 732–740, 2015.
6. Guan Hai-ling, Cheng Bao-dong. Analysis on the Consumer's online Information Search Behavior. E-Business and Information System Security, pp1 – 4, 2009.
7. RamitaVerma, ShubhkamanaRathore, PEST Analysis for Indian Luxurious Car Market, IJMSSR Volume 2, pp 22-26, 2013.
8. Nikhil Monga, Bhuvender Chaudhary, Car Market and buying behavior - A study on Consumer Perception, IJRMEC Volume 2, Issue 2, pp: 44-63, 2012.
9. Konstan JA, Riedl J. Recommender systems: from algorithms to user experience. User Model User-Adapt Interact Volume 22, Issue 1-2, and pp: 101-123, 2012.
10. Klein, L. R. , & Ford, G. T. Consumer Search for information in the digital age: An empirical study of pre-purchase search for automobiles. Journal of Interactive Marketing, Volume 17, Issue 3, pp: 29–49, 2003.
11. Raj Kumar,Dr. Rajesh VermaClassification Algorithms for Data Mining:A Survey,International Journal of Innovations in Engineering and Technology (IJIET), 2012.
12. AnoopJain, ArunaBajpai, Manish Kumar Rohila,International Journal of Emerging Technology and Advanced Engineering Website: www. ijetae. com, Volume 2, Issue 6, 2012.

Clustering Classification Automobile Information Search Behaviour