1,410 research outputs found
Optimizing K-Means Initial Number of Cluster Based Heuristic Approach: Literature Review Analysis Perspective
One popular clustering technique - the K-means widely use in educational scope to clustering and mapping document, data, and user performance in skill. K-means clustering is one of the classical and most widely used clustering algorithms shows its efficiency in many traditional applications its defect appears obviously when the data set to become much more complicated. Based on some research on K-means algorithm shows that Number of a cluster of K-means cannot easily be specified in much real-world application, several algorithms requiring the number of cluster as a parameter cannot be effectively employed. The aim of this paper describes the perspective K-means problems underlying research. Literature analysis of previous studies suggesting that selection of the number of clusters randomly cause problems such as suitable producing globular cluster, less efficient if as the number of cluster grow K-means clustering becomes untenable. From those literature reviews, the heuristic optimization will be approached to solve an initial number of cluster randomly
A New K means Grey Wolf Algorithm for Engineering Problems
Purpose: The development of metaheuristic algorithms has increased by
researchers to use them extensively in the field of business, science, and
engineering. One of the common metaheuristic optimization algorithms is called
Grey Wolf Optimization (GWO). The algorithm works based on imitation of the
wolves' searching and the process of attacking grey wolves. The main purpose of
this paper to overcome the GWO problem which is trapping into local optima.
Design or Methodology or Approach: In this paper, the K-means clustering
algorithm is used to enhance the performance of the original Grey Wolf
Optimization by dividing the population into different parts. The proposed
algorithm is called K-means clustering Grey Wolf Optimization (KMGWO).
Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To
evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019
benchmark test functions. Results prove that KMGWO is better compared to GWO.
KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization
Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank
in terms of performance. Statistical results proved that KMGWO achieved a
higher significant value compared to the compared algorithms. Also, the KMGWO
is used to solve a pressure vessel design problem and it has outperformed
results.
Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also
compared to cat swarm optimization (CSO), whale optimization algorithm-bat
algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of
performance. Also, the KMGWO is used to solve a classical engineering problem
and it is superiorComment: 15 pages. World Journal of Engineering, 202
How Can Bee Colony Algorithm Serve Medicine?
Healthcare professionals usually should make complex decisions
with far reaching consequences and associated risks in health
care fields. As it was demonstrated in other industries, the ability
to drill down into pertinent data to explore knowledge behind the
data can greatly facilitate superior, informed decisions to ensue
the facts. Nature has always inspired researchers to develop
models of solving the problems. Bee colony algorithm (BCA),
based on the self-organized behavior of social insects is one of
the most popular member of the family of population oriented,
nature inspired meta-heuristic swarm intelligence method
which has been proved its superiority over some other nature
inspired algorithms. The objective of this model was to identify
valid novel, potentially useful, and understandable correlations
and patterns in existing data. This review employs a thematic
analysis of online series of academic papers to outline BCA in
medical hive, reducing the response and computational time and
optimizing the problems. To illustrate the benefits of this model,
the cases of disease diagnose system are presented
A Clustering Approach Based on Charged Particles
In pattern recognition, clustering is a powerful technique that can be used to find the identical group of objects from a given dataset. It has proven its importance in various domains such as bioinformatics, machine learning, pattern recognition, document clustering, and so on. But, in clustering, it is difficult to determine the optimal cluster centers in a given set of data. So, in this paper, a new method called magnetic charged system search (MCSS) is applied to determine the optimal cluster centers. This method is based on the behavior of charged particles. The proposed method employs the electric force and magnetic force to initiate the local search while Newton second law of motion is employed for global search. The performance of the proposed algorithm is tested on several datasets which are taken from UCI repository and compared with the other existing methods like K-Means, GA, PSO, ACO, and CSS. The experimental results prove the applicability of the proposed method in clustering domain
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