Authors:
P. Praveen,B. Rama,DOI NO:
https://doi.org/10.26782/jmcms.2020.01.00027Keywords:
Classification,Clustering,Data mining,Divisive Methods,Mean Based Divisive Method (MB-DivClues),Abstract
The problem of mining numerical data and to propose different approaches to efficiently apply clustering to such data According to an aspect of the method the Mean Base Divisive Clustering (MB-DivClues) method is developed to categories unstructured data into various groups. The a constructive mean-based divisive clustering method is developed to reduce comparison includes several steps which includes identification of mean value from a given dataset, to find the arithmetic mean value of base cluster-infrequent attribute and storing the found mean value in a tree which is represented as root. Further the steps include comparing the objects in the dataset with the said mean value and stored in the nearest cluster. A new cluster is created to place the sorted object in new cluster. In the process of proposed method includes steps of shifting the object value to the left cluster when it is less than the mean value, shifting the object value to right cluster when it is greater than the mean value and repeating the above procedure until singleton cluster is picked from the given dataset. Wherein before applying divisive Clustering method, initially all the data objects are available in a single cluster and a mean value is calculated on the dataset.Refference:
I. A. Babenko and V. Lempitsky, “Tree Quantization for Large-Scale Similarity
Search and Classification,” in CVPR, 2015.
II. A. K. Agogino and K. Tumer. Ensemble clustering with voting active labels. Pattern
Recognition Letters, 29(14):1947–1953, 2008.
III. Brandt, “Transform coding for fast approximate nearest neighbor search in high
dimensions,” in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 2010, pp. 1815–1822.
IV. J. Wang, T. Zhang, J. Song, N. Sebe, and H. T. Shen, “A Survey on Learning to
Hash,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13,
no. 9, 2017.
V. M. Laszlo and S. Mukherjee, “Minimum Spanning Tree Partitioning
Algorithm for Micro aggregation”, IEEE Trans. on Knowledge and Data
Engg., 17(7), 2005, 902- 911.
VI. Mohamed A. Mahfouz, d M. A. Ismail. Fuzzy Relatives of the CLARANS
Algorithm With Application to Text Clustering. International Journal of
Electrical and Computer Engineering. 2009 370-377.
VII. N. Paivinen, “Clustering with a Minimum Spanning Tree of Scale- free-like
Structure”, Pattern Recognition Letters, Elsevier, 26(7), 2005, 921-930
VIII. P.Praveen, B.Rama, “An Efficient Smart Search Using R Tree on Spatial Data”,
Journal of Advanced Research in Dynamical and Control Systems, Issue
4,ISSN:1943-023x
IX. P. Praveen, B. Rama and T. Sampath Kumar, “An efficient clustering algorithm
of minimum Spanning Tree,” 2017 Third International Conference on Advances
in Electrical, Electronics, Information, Communication and Bio-Informatics
(AEEICB), Chennai, 2017, pp. 131135. doi: 10.1109/AEEICB.2017.7972398
X. Pushpa.R. Suri, Mahak. Image Segmentation With Modified K-Means Clustering
Method. International Journal of Recent Technology and Engineering 2012 176-
179.
XI. S.Vijayarani, S.Nithya. An Efficient Clustering Algorithm for Outlier Detection.
International Journal of Computer Applications. 2011 22-27.
XII. Xiaochun Wang, Xiali Wang and D. Mitchell Wilkes, “A Divide-and conquer
Approach for Minimum Spanning Tree-based Clustering”, IEEE Transactions on
Knowledge and Data Engg., 21, 2009.
XIII. Y. Chen, T. Guan, and C. Wang, “Approximate nearest neighbor search by
residual vector quantization,” Sensors, vol. 10, no. 12, pp. 11259– 11273, 2010.
XIV. 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
XV. Zhang, D. Chao, and J. Wang, “Composite Quantization for Approximate
Nearest Neighbor Search,” in ICML, 2014.