Volume 4, Issue 1-1, January 2015, Page: 9-14
Towards a Successful Startup Company: Best Successful Team Components
Teejan T. El-Khazendar, Information Technology, Islamic University of Gaza, Gaza, Palestine
Rifa J. El-Khozondar, Physics department, Al-Aqsa University, Gaza, Palestine
Received: Dec. 4, 2014;       Accepted: Dec. 8, 2014;       Published: Dec. 27, 2014
DOI: 10.11648/j.ajtas.s.2015040101.12      View  3064      Downloads  201
Abstract
Entrepreneurship became an important sector in the Arab world. A lot of young entrepreneurs have ambitious projects and creative ideas, which they hope to get fund and incubation to implement these ideas. There are three incubators in Gaza which provide the required incubation, training and fund. Entrepreneurs personality characters have a big effect on the success of their startup companies; moreover, the startup companies category plays a big role on the success of their startup companies especially in small markets such as in Gaza. So we have to find a way to discover which is the most successful ideas and under which category can be classified with paying tight attention for the characters of the team members for each idea. They should have some traits which qualify this team seems to be successful. In the present paper, we are using computing approach based on data mining techniques to study one of the business fields to produce a business technique that helps in extraction the association rules for the incubated startup companies in Gaza. Moreover, we will study these association rules to understand and help the incubators in Gaza to avoid the failed ideas and teams as possible as it could be. Therefore, the incubators will be able to improve the incubation and entrepreneurship sector and increase the number of successful startup companies in Gaza and reduce the wasted fund and time on failed startups.
Keywords
Entrepreneurship, Entrepreneurs, Incubation, Data Mining, Fund, Startup
To cite this article
Teejan T. El-Khazendar, Rifa J. El-Khozondar, Towards a Successful Startup Company: Best Successful Team Components, American Journal of Theoretical and Applied Statistics. Special Issue:Computational Statistics. Vol. 4, No. 1-1, 2015, pp. 9-14. doi: 10.11648/j.ajtas.s.2015040101.12
Reference
[1]
M. Ankerst, M. Ester, H. Kriegel, "Towards an effective cooperation of the user and the computer for classification," Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2000.
[2]
P. Berkhin, A survey of clustering data mining techniques. Grouping multidimensional data, Springer, 2006, pp. 25-71.
[3]
M. Crowne, "Why software product startups fail and what to do about it. Evolution of software product development in startup companies," Engineering Management Conference, IEEE, 2002.
[4]
H. Frigui, "Adaptive image retrieval using the fuzzy integral," Fuzzy Information Processing Society, 18th International Conference of the North American, IEEE, 1999.
[5]
P. Giudici, Applied data mining: statistical methods for business and industry, John Wiley & Sons, 2005.
[6]
D. Hunyadi, "Performance comparison of Apriori and FP-Growth algorithms in generating association rules," Proceedings of the European Computing Conference, 2011.
[7]
J. Keller, M. Gray, J. Givens, JR, "A fuzzy k-nearest neighbor algorithm," Systems, Man and Cybernetics, IEEE Transactions, Vol. 4, pp, 580-585, 1985.
[8]
K. Moin, Q. Ahmed, "Use of Data Mining in Banking," International Journal of Engineering Research and Applications, Vol. 2(2), pp. 738-742, 2012.
[9]
K. Pal, A. Ghosh, Soft computing for image processing, Heidelberg: Physica-Verlag, 2000, pp. 44-78.
[10]
W. Pinnington, L. Ben, F. Elaine, "Too Much of a Good Thing? A Field Study of Challenges in Business Intelligence Enabled Enterprise System Environments," 2007.
[11]
M. Rijmenam, "Five Data Mining Techniques That Help Create Business Value,"2014,. Retrieved from http://www.bigdata-startups.com/data-mining-techniques-create-business-value/ last visited in 5 December 2014.
[12]
S. Shafer, H. Smith, J. Linder, "The power of business models," Business horizons Vol: 48(3): pp. 199-207, 2005.
[13]
M. Spahn, J. Kleb, S., Grimm, S. Scheidl, "Supporting business intelligence by providing ontology-based end-user information self-service," Proceedings of the First international Workshop on ontology-Supported Business intelligence, ACM. October 2008, pp. 10.
[14]
G.. Weiwei, Z. Xiaodong, "Cross-Cultural Differences of Entrepreneurs' Error Orientation: Comparing Chinese Entrepreneurs and German Entrepreneurs," in Information Technology and Applications, 2010 International Forum on, IEEE, Vol. 3, 2010, pp. 198-201.
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