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首页> 外文期刊>International journal of systems assurance engineering and management >Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering
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Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering

机译：量子启发式进化方法选择模糊聚类的最优参数

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Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor ( m ) and the number of clusters ( c ) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m . From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor ( m ) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c . Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like $$V_{CWB}$$ V C W B and $$V_{OS}$$ V O S and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m , c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods.
机译：最近，由于模糊c均值（FCM）算法的效率和简便性，它得到了最广泛的应用。但是，FCM对模糊因子（m）的初始化和簇数（c）敏感，因此容易陷入局部最优。这些参数的选择是一个关键问题，因为不利的选择可能会使数据中的簇模糊。在可用的模糊聚类文献中，聚类有效性指数用于确定数据集的最佳聚类数量，但是由于随机选择m，这些指数可能会陷入局部最优值。从处理局部最优问题的角度出发，我们提出了一种混合模糊聚类方法，称为量子启发式进化模糊c-均值算法。在提出的方法中，我们将量子计算的概念与FCM集成在一起，以使参数m几代演化。具有量子概念的模糊因子（m）的演化旨在为种群多样性和较大的搜索空间提供更好的特性，以寻找m的全局最优值及其对应的c值。报告并讨论了使用三个真实世界数据集的实验。将该方法的结果与从有效性指标（例如$$ V_ {CWB} $$ V C W B和$$ V_ {OS} $$ V OS）以及基于进化模糊聚类算法获得的结果进行比较。结果表明，与最新方法相比，所提出的方法以最小的适应度函数实现了m，c的全局最优值，并且在收敛时间（迭代次数）上有显着改善。

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