Journal of Applied Information Science

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Unstructured Mining Competitors from Large Datasets


Affiliations
1 Department of Computer Science, GATE College, Tirupati, Andhra Pradesh, India
     

   Subscribe/Renew Journal


In this enterprise, accomplishment relies upon the capability to make an aspect more charming clients than the check. Different requests broaden with recognize to this endeavour: How would possibly we formalize and degree the force among things? Who are the rule contenders of a given issue? What are the features of a component that most impact its force? Despite the impact and importance of this problem to numerous spaces, most effective an obliged share of labour has been submitted toward a powerful game plan. Right now, gift a traditional significance of the forcefulness between matters, in angle to be had segments that the two of them can unfold. Our evaluation of power makes use of patron reviews, a copious wellspring of facts that is open in a wide quantity of areas [1, 2]. We gift profitable methodologies for evaluating forcefulness in a way reaching study datasets and address the trademark problem of finding the first-rate k contenders of a given issue. Finally, we evaluate the idea of our effects and the versatility of our system the use of numerous datasets from exclusive zones.

Keywords

Data Mining, Electronic Commercial Enterprise, Information Search and Retrieval, Web Mining.
Subscription Login to verify subscription
User
Notifications
Font Size


  • M. E. Porter, Competitive Strategy: Techniques for Analyzing Industries and Competitors, Free Press, 1980.

  • R. Deshpande, and H. Gatingon, “Competitive analysis,” Marketing Letters, vol. 5, pp. 271-288, 1994.

  • B. H. Clark, and D. B. Montgomery, “Managerial identification of competitors,” Journal of Marketing, vol. 63, no. 3, pp. 67-83, 1999.

  • W. T. Few, “Managerial competitor identification: Integrating the categorization, economic and organizational identity perspectives,” Doctoral dissertation, 2007.

  • M. Bergen, and M. A. Peteraf, “Competitor identification and competitor analysis: A broad-based managerial approach,” Managerial and Decision Economics, vol. 23, pp. 157-169, 2002.

  • J. F. Porac, and H. Thomas, “Taxonomic mental models in competitor definition,” The Academy of Management Review, vol. 15, no. 2, pp. 224-240, 1990.

  • M.-J. Chen, “Competitor analysis and interfirm rivalry: Toward a theoretical integration,” The Academy of Management Review, vol. 21, no. 1, pp. 100-134, 1996.

  • R. Li, S. Bao, J. Wang, Y. Yu, and Y. Cao, “CoMiner: An effective algorithm for mining competitors from the web,” In Sixth International Conference on Data Mining (ICDM’06), 2006.

  • Z. Ma, G. Pant, and O. R. L. Sheng, “Mining competitor relationships from online news: A network-based approach,” Electronic Commerce Research and Applications, vol. 10, pp. 418-427, 2011.

  • R. Li, S. Bao, J. Wang, Y. Liu, and Y. Yu, “Web scale competitor discovery using mutual information,” In International Conference on Advanced Data Mining and Applications (ADMA’06), pp. 798-808, 2006.

  • S. Bao, R. Li, Y. Yu, and Y. Cao, “Competitor mining with the web,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 10, pp. 1297-1310, 2008.

  • G. Pant, and O. R. L. Sheng, “Avoiding the blind spots: Competitor identification using web text and linkage structure,” In ICIS 2009 Proceedings, 2009.

  • D. Zelenko, and O. Semin, “Automatic competitor identification from public information sources,” International Journal of Computational Intelligence and Applications, vol. 2, no. 3, pp. 287-294, 2002.

  • R. Decker, and M. Trusov, “Estimating aggregate consumer preferences from online product reviews,” International Journal of Research in Marketing, vol. 27, no. 4, pp. 293-307, 2010.

  • C. W.-K. Leung, S. C.-F. Chan, F.-L. Chung, and G. Ngai, “A probabilistic rating inference framework for mining user preferences from reviews,” World Wide Web, vol. 14, no. 2, pp. 187-215, 2011.


Abstract Views: 197

PDF Views: 0




  • An Unstructured Mining Competitors from Large Datasets

Abstract Views: 197  |  PDF Views: 0

Authors

Divya Maradala
Department of Computer Science, GATE College, Tirupati, Andhra Pradesh, India

Abstract


In this enterprise, accomplishment relies upon the capability to make an aspect more charming clients than the check. Different requests broaden with recognize to this endeavour: How would possibly we formalize and degree the force among things? Who are the rule contenders of a given issue? What are the features of a component that most impact its force? Despite the impact and importance of this problem to numerous spaces, most effective an obliged share of labour has been submitted toward a powerful game plan. Right now, gift a traditional significance of the forcefulness between matters, in angle to be had segments that the two of them can unfold. Our evaluation of power makes use of patron reviews, a copious wellspring of facts that is open in a wide quantity of areas [1, 2]. We gift profitable methodologies for evaluating forcefulness in a way reaching study datasets and address the trademark problem of finding the first-rate k contenders of a given issue. Finally, we evaluate the idea of our effects and the versatility of our system the use of numerous datasets from exclusive zones.

Keywords


Data Mining, Electronic Commercial Enterprise, Information Search and Retrieval, Web Mining.

References