Extracting top-k Competitors from Unorganized Data

Authors

  • Sathya N Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore, India
  • Prabha RPS Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore, India
  • Shashvitha V Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore, India
  • Kiruthika G Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore, India
  • Patel MM Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore, India

DOI:

https://doi.org/10.26438/ijcse/v7i2.736742

Keywords:

C-Miner++ algorithm, Feature extraction, Mining competitors, Score calculation

Abstract

Data mining is the dominant area of consideration which makes simpler the profitable expansion evolution such as mining user preferred, mining web material ’s to get boldness about the formation or facilities and mining the competitors of an exact professional. In the fresh competitive vocation expansion, there is a necessity to analyse the competitive constructions and inspirations of an item that ultimate scratch its competitiveness. The guesstimate of competitiveness unceasingly sequences the procurer thoughts in terms of analyses, marks and a generous basis of suggestions from the net and other centers. In this technique, we extend the proper description of the competitiveness among two items, centered on the bazaar sections that they can both cover. A C-Miner++ procedure is planned that speeches the unruly of discovery the top-k competitors of an item in any given market by figuring all the sections in a given market based on excavating huge review datasets and it arises meaning of competitiveness. And also used C-Miner++ with feedback algorithm. Finally, we appraise the excellence of our outcomes and the scalability of our method using numerous datasets from dissimilar fields.

References

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Published

2019-02-28
CITATION
DOI: 10.26438/ijcse/v7i2.736742
Published: 2019-02-28

How to Cite

[1]
N. Sathya, R. S. Prabha, V. Shashvitha, G. Kiruthika, and M. M. Patel, “Extracting top-k Competitors from Unorganized Data”, Int. J. Comp. Sci. Eng., vol. 7, no. 2, pp. 736–742, Feb. 2019.

Issue

Vol. 7 No. 2 (2019): IJCSE February Edition

Section

Survey Article

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