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.
| Published in |
American Journal of Theoretical and Applied Statistics (Volume 4, Issue 1-1)
This article belongs to the Special Issue Computational Statistics |
| DOI | 10.11648/j.ajtas.s.2015040101.12 |
| Page(s) | 9-14 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Entrepreneurship, Entrepreneurs, Incubation, Data Mining, Fund, Startup
| [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. |
APA Style
Teejan T. El-Khazendar, Rifa J. El-Khozondar. (2014). Towards a Successful Startup Company: Best Successful Team Components. American Journal of Theoretical and Applied Statistics, 4(1-1), 9-14. https://doi.org/10.11648/j.ajtas.s.2015040101.12
ACS Style
Teejan T. El-Khazendar; Rifa J. El-Khozondar. Towards a Successful Startup Company: Best Successful Team Components. Am. J. Theor. Appl. Stat. 2014, 4(1-1), 9-14. doi: 10.11648/j.ajtas.s.2015040101.12
AMA Style
Teejan T. El-Khazendar, Rifa J. El-Khozondar. Towards a Successful Startup Company: Best Successful Team Components. Am J Theor Appl Stat. 2014;4(1-1):9-14. doi: 10.11648/j.ajtas.s.2015040101.12
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Download@article{10.11648/j.ajtas.s.2015040101.12,
author = {Teejan T. El-Khazendar and Rifa J. El-Khozondar},
title = {Towards a Successful Startup Company: Best Successful Team Components},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {4},
number = {1-1},
pages = {9-14},
doi = {10.11648/j.ajtas.s.2015040101.12},
url = {https://doi.org/10.11648/j.ajtas.s.2015040101.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.s.2015040101.12},
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.},
year = {2014}
}
TY - JOUR T1 - Towards a Successful Startup Company: Best Successful Team Components AU - Teejan T. El-Khazendar AU - Rifa J. El-Khozondar Y1 - 2014/12/27 PY - 2014 N1 - https://doi.org/10.11648/j.ajtas.s.2015040101.12 DO - 10.11648/j.ajtas.s.2015040101.12 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 9 EP - 14 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.s.2015040101.12 AB - 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. VL - 4 IS - 1-1 ER -
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