An Implementation of Maintaining Database and Generating Report using K-Means Algorithm

Authors

  • Prashant P. Kulkarni Department of Information Technology, Rajarshi Shahu College of Engineering, Pune, India
  • Dipali H. Patil Department of Information Technology, Rajarshi Shahu College of Engineering, Pune, India
  • Akshata V. Ghodke Department of Information Technology, Rajarshi Shahu College of Engineering, Pune, India
  • Tejashri S. Pansare Department of Information Technology, Rajarshi Shahu College of Engineering, Pune, India
  • Javed I. Tamboli Department of Information Technology, Rajarshi Shahu College of Engineering, Pune, India

DOI:

https://doi.org/10.37591/jowet.v1i1.54

Abstract

This is a sponsored project from Electronics Testing and Development Center (ETDC), a government organization which works under Standardization Testing and Quality Certification (STQC), Government of India, and provides quality assurance services in the area of electronics and IT through countrywide network of laboratories and centers. Automation of testing and calibration of electronic instruments will assist ETDC in providing these services in a better way by generating report and maintaining history of customer and electronic instruments on web-based paperless system. For generating report, the authors have applied data-mining algorithm as k-means algorithm.

Keywords: K-means algorithm, data mining

References

Various ETDC Documents and Registers.

Ubes R, Jain AK. Clustering techniques: The users dilemma. Pattern Recognition. 1976; 8: 247–60p.

Kamber Micheline. Data Mining: Concepts and Techniques.

Steinbach Tan, Kumar. Introduction to Data Mining.

Joaqu´ın P´erez, Adriana Mexicano, Ren´e Santaolaya, et al. Improvement to the K-means algorithm through a heuristics based on a bee honeycomb structure. Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC). 2012.

Shi Na, Liu Xumin, Guan Yong. Research on k-means clustering algorithm: An improved k-means clustering algorithm. Third International Symposium on Intelligent Information Technology and Security Informatics. IEEE 2010, 63–67p.

Abdul Nazeer KA, Madhu Kumar SD, Sebastian MP. Enhancing the k-means clustering algorithm by using a O(n logn) heuristic method for finding better initial centroids. Second International Conference on Emerging Applications of Information Technology. 2011.

Faliu Yi, Inkyu Moon. Extended k-means algorithm. Fifth International Conference on Intelligent Human-Machine Systems and Cybernetics. 2013.

Xu R. Survey of clustering algorithms. IEEE Trans. Neural Netw. May 2005; 16: 645–78p.

Chen X, Yin W, Tu P, et al. Weighted k-means algorithm based text clustering. Proceedings of International Symposium on Information Engineering and Electronic Commerce. May 2009; 51–5p.

Zhao X, Wang Y, Zhan Y, et al. Optimization of k-means clustering by feature weight learning. Journal of Computer Research and Development (in Chinese). June 2003; 40: 869–73p.

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Published

2015-12-28

Issue

Vol. 1 No. 1 (2014)

Section

Research Articles

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