A Proposed Algorithm for Clustering Data Based on Relations Among Points
DOI:
https://doi.org/10.32792/jeps.v10i1.28الكلمات المفتاحية:
Clustering، K-means Algorithm، Hierarchical Clustering Algorithm، Proposed Clustering Algorithm، CURE Clustering Algorithm، Partitioning Clustering Algorithmالملخص
This paper deals with automatic clustering algorithm, which partitions a many of objects to get smaller sets (clusters). Where objects of the one set are related to each other rather than to those in the other sets. The number of clusters in automatic clustering don’t given priori, they determinates automatically, the number resulted of clusters exact closed to the numbers of the real dataset structure.This paper represents a stimulus for the current study to introduce an algorithm that automatically finds the number of clusters based on distance among vertices. The study is based on the hypothesis that the proposed algorithm is able to efficiently find the clustering partitions for the whole dataset
المراجع
K. Rajalakshmi, Dr.S.S. Dhenakaran, N. Roobin “Comparative Analysis of K-Means Algorithm in
Disease Prediction”, International Journal of Science, Engineering and Technology Research (IJSETR),
Volume 4, Issue 7, July 2015
Shalini Goel. “Improved K-mean Clustering Algorithm for Prediction Analysis using
Classification Technique in Data Mining”. International Journal of Computer Applications
· January 2017.
Shital A. Raut and S. R. Sathe, “A Modified Fastmap K-Means Clustering Algorithm for Large Scale
Gene Expression Datasets”, International Journal of Bioscience, Biochemistry and Bioinformatics, Vol.
, No. 4, page 120-124, November 2011.
Daljit Kaur and Kiran Jyot, “Enhancement in the Performance of K-means Algorithm”, International
Journal of Computer Science and Communication Engineering, Volume 2 Issue 1, 2013.
Siddheswar Ray and Rose H. Turi, “Determination of Number of Clusters in K-Means Clustering and
Application in Colour Image Segmentation”, School of Computer Science and Software Engineering
Monash University, Wellington Road, Clayton, Victoria, 3168, Australia, 1999
Azhar Rauf, Mahfooz, Shah Khusro and Huma Javed “Enhanced K-Mean Clustering Algorithm to
Reduce Number of Iterations and Time Complexity”, Middle- East Journal of Scientific Research 12 (7):
-963, 2012 ISSN 1990-92332012
Madhu Yedla, T M Srinivasa, “Enhancing K-means Clustering Algorithm with Improved Initial
Center”, International Journal of Computer Science and Information Technologies, Vol. 1 (2) 2010, page
-125
Osamor VC, Adebiyi EF, Oyelade JO and Doumbia S “Reducing the Time Requirement of K-Means
Algorithm” PLoS ONE, Volume 7, Issue 12, pp-56-62, 2012.
Akhilesh Kumar Yadav, Divya Tomar, Sonali Agarwal, “Clustering of Lung Cancer Data Using Foggy
K-Means”, International Conference on Recent Trends in Information Technology (ICRTIT) 2013
Sanjay Chakrabotry, Prof. N.K Nigwani and Lop Dey “Weather Forecasting using Incremental Kmeans
Clustering”, 2014
Chew Li Sa; Bt Abang Ibrahim, D.H.; Dahliana Hossain, E.; bin Hossin, M., "Student performance
analysis system (SPAS)," in Information and Communication Technology for The Muslim World
(ICT4M), 2014 The 5th International Conference on , vol., no., pp.1-6, 17-18 Nov. 2014
Abdelghani Bellaachia, Erhan Guven, “Predicting Breast Cancer Survivability Using Data Mining
Techniques”, Washington DC 20052, 2010
Qasem a. Al-Radaideh, Adel Abu Assaf 3eman Alnagi, “Predictiong Stock Prices Using Data Mining
Techniques”, The International Arab Conference on Information Technology (ACIT’2013)
K. A. Abdul Nazeer, M. P. Sebastian, “Improving the Accuracy and Efficiency of the k-means
Clustering Algorithm, Vol IWCE 2009, July 1 - 3, 2009, London, U.K
. A. Gionis, H. Mannila, and P. Tsaparas, Clustering aggregation. ACM Transactions on Knowledge
Discovery from Data (TKDD), 2007. 1(1): p. 1-30.
. H. Chang and D.Y. Yeung, Robust path-based spectral clustering. Pattern Recognition, 2008. 41(1):
p. 191-203
. Jassim T. Sarsoh, Kadhem M. Hashim, Firas S. Miften, “Comparisons between Automatic and Non-
Automatic Clustering Algorithms”, Journal of College of Education for Pure Sciences Vol. 4 No.1.
Anton Riabov, Zhen Liu, Joel L. Wolf, Philip S. Yu and Li Zhang. “Clustering Algorithms
for Content-Based Publication-Subscription Systems”, in Proceedings of the 22 nd International
Conference on Distributed Computing Systems (ICDCS’02) ©, IEEE Computer Society Washington, DC,
USA, Page 133, 2002.
Cen Li and Gautam Biswas. “Unsupervised Learning with Mixed Numeric and NominalData”. IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 4, Pages: 673
– 690, July/August 2002.
Eamonn Keogh, Kaushik Chakrabarti, Michael Pazzani and Sharad Mehrotra. “Dimensionality
Reduction for Fast Similarity Search in Large Time Series Databases”. Knowledge and Information
Systems, Vol. 3, Pages 263–286, 2001
Lepere R. and Trystram D. “A New Clustering Algorithm for Large Communication Delays” in
Proceedings of16th IEEE-ACM Annual International Parallel and Distributed Processing Symposium
(IPDPS’02), Fort Lauderdale, USA, 2002.
Rui Xu and Donald C. Wunch,"Survey of Neural Network", Vol.16, No.3, 2005.
Jain A., and Dubes R., “Algorithms for Clustering Data”, Neural Computer Survey., Vol.37, No.12,
Everitt B., Landau S., and Leece M., “Cluster Analysis”, London, Arnold, 2001
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