Analysis of K-Mean Clustering For Various Data Sets In MATLAB

Authors

  • Varsha Bansal  Ganga Institute of Technology and Management, MDU Rohtak, Maharashtra, India
  • Mahesh Kumar  Assistant. Professor, Department of Computer Science and Engineering, GITAM, MDU Rohtak, Maharashtra, India

Keywords:

K-Mean, Global Variables , MATLAB.

Abstract

Data mining is a process that uses different technologies, using "patterns" or "knowledge" in the data. These are some data mining techniques related to classification, clustering, and association principles. Among them there are some "similar" things between them, called "balanced" Belongs to other clusters. This means using cluster analysis to find things projects in one group will be different from projects and other group objects. Is a cluster Neutral learning skills. Neutral learning means learning first without knowledge Sample rating K-minerals are a specific way of cluster observations. "K" indicates the number of specific clusters. There are different distance steps to determine Observe the cluster. The purpose of the algorithm is to minimize Measurements between cluster centers and their given observations Track observations in any cluster and eliminate shortest distance measurements Get, finish. This proposed work having global data sets work on MATLAB which found very effectively when it come to underlying structure of modified K-mean. This will have the capability of finding the cluster area efficiently.

References

  1. Michael Steinbach,George Karypis ,Vipin Kumar, “AComparison of Document Clustering Techniques”.
  2. Kris Buchanan, Daniel Gaytan, Lu Xu, Chris Dilay, and David Hilton, “Spatial K-means Clustering of HF Noise Trends in Southern California Waters”.
  3. Magnus Oskarsson, “Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering” , J Math Imaging Vis (2017) 57: pp. 225–238, 2016.
  4. Deruo Cheng,  Yiqiong Shi, Tong Lin, “Hybrid K-Means Clustering and Support Vector Machine Method for Via and Metal Line Detections in Delayered IC Images” IEEE, 2018
  5. Tao Lei, Xiaohong Jia, Yanning Zhang, Lifeng He, “Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering”, IEEE, 2017.
  6. Ekta Joshi, Dr. D. A. Parikh, “An Improved K-Means Clustering Algorithm” (4)2 : 239-244, 2018.
  7. Arpit Bansal, Mayur Sharma, Shalini Goel, “Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining”, International Journal of Computer Applications (0975 – 8887) Volume 157 – No 6, January 2017.
  8.  Marco Capóa, Aritz Pérez a , Jose A. Lozanoa, “An efficient approximation to the K-means clustering for massive data”, Knowledge-Based Systems 000 (2016) 1–14, 2016.
  9.  Yanhui Guo1 , Rong Xia2 , Abdulkadir S¸ engu¨r , Kemal Polat, “A novel image segmentation approach

based on neutrosophic c-means clustering and indeterminacy filtering”, 2016.

  1. Takayuki Iguchi1 , Dustin G. Mixon1 ,Jesse Peterson1 ,Soledad Villar, “Probably certifiably correct k-means clustering”, 2016.
  2. ]S. Javadi , S.M. Hashemy  , K. Mohammadi  , K.W.F. Howard  , A. Neshat, “Classification of aquifer vulnerability using K-means cluster analysis”, Journal of Hydrology 549 (2017) 27–37, 2017.
  3.  Sina Khanmohammadi, Naiier Adibeig, Samaneh Shanehbandy, “An Improved Overlapping k-Means Clustering Method for Medical Applications”, September 16, 2016.
  4.  Hongfu Liu, Junjie Wu, Tongliang Liu, Dacheng Tao, and Yun Fu, “Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence”, Ieee Transactions On Knowledge And Data Engineering, Vol. Xxx, No. Xxx, January 2017.
  5. Jiahu Qin, Weiming Fu, Huijun Gao, Wei Xing Zheng, “Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory”, IEEE, 2016.
  6.  Tian-Shi Xu, Hsiao-Dong Chiang, Guang-Yi Liu, and Chin-Woo Tan, “Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data”,IEEE, 2015.
  7. Miin-Shen Yanga , Yessica Nataliani, “Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters”,2017.

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Published

2018-06-30

Issue

Section

Research Articles

How to Cite

[1]
Varsha Bansal, Mahesh Kumar, " Analysis of K-Mean Clustering For Various Data Sets In MATLAB, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.139-143, May-June-2018.