Finding the optimal
number of clusters has remained to be a challenging problem in data mining
research community. Several approaches have been suggested which include
evolutionary computation techniques like genetic algorithm, particle swarm
optimization, differential evolution etc. for addressing this issue. Many
variants of the hybridization of these approaches also have been tried by
researchers. However, the number of optimal clusters and the computational
efficiency has still remained open for further research. In this paper, a new
optimization technique known as “Teaching-Learning-Based Optimization” (TLBO)
is implemented for automatic clustering of large unlabeled data sets. In
contrast to most of the existing clustering techniques, the proposed algorithm
requires no prior knowledge of the data to be classified rather it determines the
optimal number of partitions of the data “on the run”. The new AUTO-TLBO
algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made
with some well-known clustering algorithms. Results show that AUTO-TLBO
clustering techniques have much potential in terms of comparative results and
time of computations.
Cite this paper
Murty, M. , Naik, A. , Murthy, J. , Reddy, P. , Satapathy, S. and Parvathi, K. (2014) Automatic Clustering Using Teaching Learning Based Optimization. Applied Mathematics
, 1202-1211. doi: 10.4236/am.2014.58111
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