Date of Award
2015
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Ludwig, Simone
Description
Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image segmentation and pattern classification. The enlarging volumes of information emerging by the progress of technology, makes clustering of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel clustering algorithms. In this paper, we propose a parallel k-means++ clustering algorithm based on MapReduce, which is simple like traditional K-means, yet more powerful because the initial centroid selection process is not random. It follows a formula to plot initial centroids at equal distance and then iterates repeatedly like k-means to converge and produce final cluster. This makes this algorithm faster and parallelizing makes it more scalable. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets.
Recommended Citation
Bhattacharyya, Krittika, "A Map Reduce Approach of K-Means++ Algorithm with Initial Equidistant Centers" (2015). Computer Science. 176.
https://digitalcommons.ndsu.edu/computer-sci/176
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/25350