A Proposed Algorithm for Clustering Data Based on Relations Among Points


  • Firas S. Miften Miften 2 University of Thi-Qar, College of Education for Pure Science, Computer Science Department
  • Mustafa A. Naser 1 Ministry of Education, Thi-Qar Education Directorate


Clustering, K-means Algorithm, Hierarchical Clustering Algorithm, Proposed Clustering Algorithm, CURE Clustering Algorithm, Partitioning Clustering Algorithm


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


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


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