Minimum Spanning Tree-Resembling algorithm for Clusters, Outliers and Hubs

References

• Anil K. Jain, Richard C. Dubes “Algorithm for Clustering Data”, Michigan State University, Prentice Hall, Englewood Cliffs, New Jersey 07632.1988.
   Google Scholar
• F. Angiulli, and C. Pizzuti, “Outlier Mining in Large High-Dimensional Data sets”, IEEE Transactions on Kaoeledge and Data Engineering, 17(2): 203–215, 2005.
   Web of Science ®Google Scholar
• V. Barnett and T. Lewis, “Outliers in Statistical Data”., John Wiley, 1994.
   Google Scholar
• M. Breunig, H. Kriegel, R. Ng and J. Sander, “Lof: Identifying density-based local outliers”, In Proceedings of 2000 ACM SIGMOD International Conference on Management of Data. ACM Press, pp 93–104, 2000.
   Google Scholar
• N. Chowdhury and C.A. Murthy, “Minimum Spanning Tree –Based Clustering Techniques: Relationship with Bayes Classifier”, Pattern Recognition, Vol. 30, no 11, pp 1919–1929, 1997.
   Web of Science ®Google Scholar
• B. Custem and I. Gath, “Detection of Outliers and Robust Estimation using Fuzzy clustering”, Computational Statistics & data Analyses15,pp.47–61, 1993.
   Web of Science ®Google Scholar
• Ester, M. Kriegel, H.P. Sander, J. and Xu, X. “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 226–231, 1996.
   Google Scholar
• B. Ghosh-Dastidar and J.L. Schafer, ”Outlier Detection and Editing Procedures for Continuous Multivariate Data”. ORP Working Papers, September 2003.
   Google Scholar
• S. Guha, R. Rastogi, and K. Shim. “CURE an efficient clustering algorithm for large databases”. In Proceeding of the 1998 ACM SIGMOD Int. Conf. on Management of Data, pp 73–84, Seattle, Washington, 1998.
   Google Scholar
• Han, J. and Kamber, M. “Data Mining: Concepts and Techniques”, The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series editor Morgan Kaufmann Publishers, 550 pages, 2000.
   Google Scholar
• D. Hawkins, “Identifications of Outliers”, Chapman and Hall, London, 1980.
   Google Scholar
• A. Hinneburg and D.A. Keim, “An Efficient Approach to Clustering in Large Multimedia Databases with Noise,” In Proc. Of th 4th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 58–65. 1998.
   Google Scholar
• A. Hinneburg and D.A. Keim, “A General Approach to Clustering in Large Databases with Noise,” Knowledge and Information Systems (KAIS), vol. 5, no. 4, pp. 387–415, 2003.
   Google Scholar
• Hubert L. J “Min and max hierarchical clustering using asymmetric similarity measures” Psychometrika38, 63–72, 1973.
   Web of Science ®Google Scholar
• S. John Peter, S.P. Victor, “A Novel Algorithm for Meta similarity clusters using Minimum spanning tree”. International Journal of computer science and Network Security. Vol.10 No.2 pp. 254–259, 2010.
   Google Scholar
• S. John Peter, “Nonparametric Search Algorithm for Outliers and Hubs Using Minimum Spanning Tree”, Journal of Discrete Mathematical Sciences and Cryptography Vol 14, No. 5 pp 489–502, 2011.
   Google Scholar
• S. John Peter, “Detection of Outliers and Hubs Using Minimum Spanning Tree Based on Analytical perspective of Degree Numbers”, Journal of Discrete Mathematical Sciences and Cryptography Vol 14, No. 5, pp 475–488, 2011
   Google Scholar
• Kaufman, L. and Rousseeuw, P. “Finding Groups in Data: An Introduction to Cluster Analysis”. John Wiley Sons, New York, USA, 1990.
   Google Scholar
• T. Karthikeyan, S. John Peter, “Detection of outliers and Hubs using Minimum Spanning Tree based on Node connectivity”, International Journal of Advanced Research in Computer Science ISSN 0976-5697 Vol 2, No 3, pp 340–345, 2011.
   Google Scholar
• E. Knorr and R. Ng, “A Unified Notion of Outliers: Properties and Computation”, In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 219–222, August 1997.
   Google Scholar
• E. Knorr, R. Ng and V. Tucakov, “Distance- Based Outliers: Algorithms and Applications”, VLDB Journal, 8(3-4):237–253, 2000.
   Web of Science ®Google Scholar
• K. Niu, C. Huang, S. Zhang and J. Chen, “ODDC: Outlier Detection Using Distance Distribution Clustering”, T. Washio et al. (Eds.): PAKDD 2007 Workshops, Lecture Notes in Artificial Intelligence (LNAI)4819, pp.332–343, Springer-Verlag, 2007.
   Google Scholar
• M. Laszlo and S. Mukherjee, “Minimum Spanning Tree Partitioning Algorithm for Micro aggregation”, IEEE Trans, Knowledge and Data Eng, Vol. 17, no 7, pp 902–911, July 2005.
   Web of Science ®Google Scholar
• A. Loureiro, L. Torgo and C. Soares, “Outlier detection using Clustering methods: A data cleaning Application”, in Proceedings of KDNet Symposium on Knowledge-based systems for the Public Sector. Bonn, Germany, 2004.
   Google Scholar
• Oleksandr Grygorash, Yan Zhou, Zach Jorgensen. “Minimum spanning Tree Based Clustering Algorithms”. Proceedings of the 18^th IEEE International conference on tools with Artificial Intelligence (ICTAI'06) 2006.
   Google Scholar
• S. Papadimitriou, H. Kitawaga, P. Gibbons and C. Faloutsos, “LOCI: Fast outlier detection using the local correlation integral”, Proc. Of the International Conference on Data Engineering ,pp.315–326, 2003.
   Google Scholar
• S. Ramaswamy, R. Rastogi and K. Shim, “Efficient Algorithms for Mining Outliers from Large Data Sets”, In Proceedings of the 2000 ACM SIGMOD InternationalConference on Management of Data, Volume 29, Issue 2, pages 427–438, May 2000.
   Google Scholar
• Sriganesh Srihari, Hoong Kee Ng, Kang Ning and Hon Wai Leong “Detecting Hubs and Quasi Cliques in Scale free Network”, IEEE 2008.
   Google Scholar
• Stefan Wuchty and Peter F. Stadler. “Centers of Complex Networks”. 2006.
   Google Scholar
• A. Vathy-Fogarassy, A. Kiss, and J. Abonyi, “Hybrid Minimal Spanning Tree and Mixture of Gaussians Based Clustering Algorithms”, Proc. IEEE Intl Conf. Tools with Artificial Intelligence, pp 73–81, 2006.
   Google Scholar
• Xiaochun Wang, Xiali Wang and Mitchell Wilkes, “A Divide-and-Conquer Approach for Minimum Spanning Tree Based Clustering”, IEEE Trans, Knowledge and Data Eng, Vol. 21 no 7, pp 945–958, July 2009.
   Web of Science ®Google Scholar
• Xiaowei Xu, Nurcan Yuruk Zhidan Feng and Thomas A.J. Schweiger, “SCAN: A Structural Clustering Algorithm for Networks”, SIGKDD, San Jose, CA, US, 2007.
   Google Scholar
• K. Niu, C. Huang, S. Zhang and J. Chen, “ODDC: Outlier Detection Using Distance Distribution Clustering”, T. Washio et al. (Eds.): PAKDD 2007 Workshops, Lecture Notes in Artificial Intelligence (LNAI)4819, pp.332–343, Springer-Verlag, 2007.
   Google Scholar
• M. Laszlo and S. Mukherjee, “Minimum Spanning Tree Partitioning Algorithm for Micro aggregation”, IEEE Trans, Knowledge and Data Eng, Vol. 17, no 7, pp 902–911, July 2005.
   Web of Science ®Google Scholar
• A. Loureiro, L. Torgo and C. Soares, “Outlier detection using Clustering methods: A data cleaning Application”, in Proceedings of KDNet Symposium on Knowledge-based systems for the Public Sector. Bonn, Germany, 2004.
   Google Scholar
• Oleksandr Grygorash, Yan Zhou, Zach Jorgensen. “Minimum spanning Tree Based Clustering Algorithms”. Proceedings of the 18th IEEE International conference on tools with Artificial Intelligence (ICTAI'06) 2006.
   Google Scholar
• S. Papadimitriou, H. Kitawaga, P. Gibbons and C. Faloutsos, “LOCI: Fast outlier detection using the local correlation integral”, Proc. Of the International Conference on Data Engineering,pp.315–326, 2003.
   Google Scholar
• S. Ramaswamy, R. Rastogi and K. Shim, “Efficient Algorithms for Mining Outliers from Large Data Sets”, In Proceedings of the 2000 ACM SIGMOD InternationalConference on Management of Data, Volume 29, Issue 2, pages 427–438, May 2000.
   Google Scholar
• Sriganesh Srihari, Hoong Kee Ng, Kang Ning and Hon Wai Leong “Detecting Hubs and Quasi Cliques in Scale free Network”, IEEE 2008.
   Google Scholar
• Stefan Wuchty and Peter F. Stadler. “Centers of Complex Networks”. 2006.
   Google Scholar
• A. Vathy-Fogarassy, A. Kiss, and J. Abonyi, “Hybrid Minimal Spanning Tree and Mixture of Gaussians Based Clustering Algorithms”, Proc. IEEE Intl Conf. Tools with Artificial Intelligence, pp 73–81, 2006.
   Google Scholar
• Xiaochun Wang, Xiali Wang and Mitchell Wilkes, “A Divide-and-Conquer Approach for Minimum Spanning Tree Based Clustering”, IEEE Trans, Knowledge and Data Eng, Vol. 21 no 7, pp 945–958, July 2009.
   Web of Science ®Google Scholar
• Xiaowei Xu, Nurcan Yuruk Zhidan Feng and Thomas A.J. Schweiger, “SCAN: A Structural Clustering Algorithm for Networks”, SIGKDD, San Jose, CA, US, 2007.
   Google Scholar
• Y. Xu, V. Olman and D. Xu. “Minimum spanning trees for gene expression data clustering”. Genome Informatics,12:24–33, 2001.
   PubMedGoogle Scholar
• K. Yoon, O. Kwon and D. Bae, “An approach to outlier Detection of Software Measurement Data using the K-means Clustering Method”, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), Madrid.pp.443–445, 2007.
   Google Scholar
• Yu-Chen Song, J.O 'Grady,G.M.P.O 'Hare, Wei Wang, “A Clustering Algorithm incorporating Density and Direction”, IEEE Computer Society, CIMCA 2008.
   Google Scholar
• Y. Wang, D. Chakrabarti, C. Wang and C. Faloutsos, “Epidemic Spreading in Real Networks: An Eigen value Viewpoint”, SRDS 2003 (pages 25–34), Florence, Italy.
   Google Scholar
• C. Zahn. “Graph-theoretical methods for detecting and describing gestalt clusters”. IEEE Transactions on Computers, C-20:68–86, 1971.
   Web of Science ®Google Scholar
• J. Zhang and N. Wang, “Detecting outlying subspaces for high-dimensional data: the new task”, Algorithms and Performance, Knowledge and Information Systems, 10(3):333–555, 2006.
   Web of Science ®Google Scholar
• Zhou, S., Zhao, J. “A Neighborhood-Based Clustering Algorithm”. PAKD 2005, LNAI 3518 361-371, 1982.
   Google Scholar