Time Complexity improvement of the First Processing stage of the Intelligent Clustering
Abstract
A new approach for data clustering is presented. IC clustering [1] initial processing stage is changed, so that the interval between the smallest and the largest radius-vector is divided into k equal sub-intervals. Each sub-interval is associated to a cluster. Depending on which sub-interval a radius-vector belongs, it is initially distributed within a cluster, associated with that sub-interval.
Literaturhinweise
D. Stojanov (2012): IC: Intelligent Clustering, a new time efficient data partitioning methodology. International Journal of Computer Science and Information Technologies 3(5), pp. 5065-5067.
J. MacQueen (1967): Some Methods for classification and Analysis of Multivariate Observations. In Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297.
L. Kaufman and P. Rousseeuw (1990): Finding Groups in Data, An Introduction to Cluster Analysis, 99th Edition. Willey-Interscience.
L. Kaufman and P. Rousseeuw (1987): Clustering by means of medoids. In Statistical Data Analysis Based on the L1 Norm, pp. 405-416.
R. Ng and J. Han (1994): Efficient and effective clustering methods for spatial data mining. In Proc. of the 20th VLDB Conference, pp. 144–155.
