Iterative shrinking method for generating clustering

An Iterative Non-parametric Clustering Algorithm Based on Local Shrinking

In this paper, we propose a new non-parametric clustering method based on local shrinking. Each data point is transformed in such a way that it moves a specific distance toward a cluster center. The direction and the associated size of each movement are determined by the median of its K-nearest neighbors. This process is repeated until a pre-defined convergence criterion is satisfied. The optim...

Latus: A new accelerator for generating combined iterative methods in solving nonlinear equation

Iterative shrinking method for clustering problems

Agglomerative clustering generates the partition hierarchically by a sequence of merge operations. We propose an alternative to the merge-based approach by removing the clusters iteratively one by one until the desired number of clusters is reached. We apply local optimization strategy by always removing the cluster that increases the distortion the least. Data structures and their update strat...

Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms

UCTTP is a NP-hard problem, which must be performed for each semester frequently. The major technique in the presented approach would be analyzing data to resolve uncertainties of lecturers’ preferences and constraints within a department in order to obtain a ranking for each lecturer based on their requirements within a department where it is attempted to increase their satisfaction and develo...

Olli Virmajoki Pairwise Nearest Neighbor Method Revisited

The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects the cluster pair to be merged so that it increases the given objective function value least. The main drawback ...