Poisson Distribution Based Initialization for Fuzzy Clustering

Abstract

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Knowledge discovery from consumer behavior in electronic home appliances market in Chennai by using data mining techniques

“…The most common methodology to clustering includes the hierarchical clustering and k-means clustering (Vintr et al, 2012). The best market conditions for successful segmentation seem to depend on three factors: potentiality, accessibility and size of prospective segment.…”

Spatio-temporal representation for long-term anticipation of human presence in service robotics

“…3) Poisson Distribution Based Initialization: Different random initializations that are usually understood as default for clustering algorithms led to poor performance of the tested methods, especially with a higher dimension of the warped hyperspace-time. The Poisson distribution based initialization proposed in [33] notably improved the computational stability of clustering over the space-hypertime. According to the instructions, we chose λ c vectors from @ X and eliminated (λ − 1) c redundant vectors using Algorithm 1.…”

Highly robust methods in data mining

“…The most common approaches to clustering include the agglomerative hierarchical clustering and k-means clustering, which both suffer from the presence of outliers in the data and strongly depend on the choice of the particular method. Some approaches are also sensitive to the initialization of the random algorithm (Vintr et al , 2012). Robust versions of the clustering have been recently studied in the context of molecular genetics, but they have not penetrated to management applications yet.…”