Computational Time Analysis of K-mean Clustering Algorithm

  Praveen Kumari,  Hakam Singh,  Pratibha Sharma

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. In the area of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. The aim this research is to analyze the computation time of k-mean clustering by varying the sample rate using stopwatch for time measurement.

Keywords- data mining, clustering, and k-mean clustering.

Unique Identification Number - IJEDR1702232
Page Number(s) - 1481-1486
Pubished in - Volume 5 | Issue 2 | May 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Praveen Kumari,  Hakam Singh,  Pratibha Sharma,   "Computational Time Analysis of K-mean Clustering Algorithm", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 2, pp.1481-1486, May 2017, Available at :http://www.ijedr.org/papers/IJEDR1702232.pdf