Abstract:In cluster analysis, the goal has always been to extemporize the best possible means of automatically determining the number of clusters. However, because of lack of prior domain knowledge and uncertainty associated with data objects characteristics, it is challenging to choose an appropriate number of clusters, especially when dealing with data objects of high dimensions, varying data sizes, and density. In the last few decades, different researchers have proposed and developed several nature-inspired metaheu… Show more
“…In addition, a comparative study shows that the FAPSO hybrid outperformed FAIWO, FAABC, and FATLBO. On the contrary, FATKBI seems to have relative equivalent performance in terms of speed and clustering solutions [38].…”
With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are one of the major drawbacks in clustering analysis, especially when the data have many dimensions and the number of clusters is too large and hard to guess. Furthermore, finding a similarity measure that preserves the functionality and represents both the composition and distribution of the bases in a DNA sequence is one of the main challenges in computational biology. In this paper, a new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Pulse coupled neural network (PCNN) is utilized for the calculation of DNA sequence similarity or dissimilarity. Bat algorithm is hybridized with the well-known genetic algorithm to solve the automatic data clustering problem. Extensive computational experiments are conducted on the expanded human oral microbiome database (eHOMD). A comparative study between the experimental results shows that the proposed hybrid algorithm achieved superior performance over the standard genetic algorithm and bat algorithm. Moreover, the hybrid performance was compared with competing algorithms from the literature review to ascertain its superiority. Mann-Whitney-Wilcoxon rank-sum test is conducted to statistically validate the obtained clusters.
“…In addition, a comparative study shows that the FAPSO hybrid outperformed FAIWO, FAABC, and FATLBO. On the contrary, FATKBI seems to have relative equivalent performance in terms of speed and clustering solutions [38].…”
With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are one of the major drawbacks in clustering analysis, especially when the data have many dimensions and the number of clusters is too large and hard to guess. Furthermore, finding a similarity measure that preserves the functionality and represents both the composition and distribution of the bases in a DNA sequence is one of the main challenges in computational biology. In this paper, a new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Pulse coupled neural network (PCNN) is utilized for the calculation of DNA sequence similarity or dissimilarity. Bat algorithm is hybridized with the well-known genetic algorithm to solve the automatic data clustering problem. Extensive computational experiments are conducted on the expanded human oral microbiome database (eHOMD). A comparative study between the experimental results shows that the proposed hybrid algorithm achieved superior performance over the standard genetic algorithm and bat algorithm. Moreover, the hybrid performance was compared with competing algorithms from the literature review to ascertain its superiority. Mann-Whitney-Wilcoxon rank-sum test is conducted to statistically validate the obtained clusters.
“…Therefore, the firefly algorithm formulation is based on three ideal rules as follows: the equation of the firefly movement is given in Eq. ( 18) [27,28] and it represents the movement of a firefly ๐ to another, more attractive firefly ๐, Eq. ( 19) [27,28] describes a firefly's attractiveness, and Eq.…”
“…( 18) [27,28] and it represents the movement of a firefly ๐ to another, more attractive firefly ๐, Eq. ( 19) [27,28] describes a firefly's attractiveness, and Eq. ( 20) [27,28] calculates the distance between firefly ๐ and firefly ๐.…”
“…It is equally important to note that the main benefits of utilizing metaheuristics to solve complex optimization problems include the algorithms' ability to easily handle complex constraints present in real-life applications and produce high-quality solutions while requiring shorter computational time [29,75,76,77,78]. Each of the algorithms was adapted or modified and applied to the problem at hand.…”
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