Flower Pollination Algorithm with Bee Pollinator for cluster analysis

• The k-Means clustering algorithm is highly depends on the initial solution and is easy to trap into the local optimal.
• Flower Pollination Algorithm is a novel approach for multi-objective optimization.
• Discard pollen operator and crossover operator are applied to increase diversity of the population, and local searching ability is enhanced by using elite based mutation operator.
• Compared with DE, CS, ABC, PSO, FPA and k-Means, the experiment results show that Flower Pollination Algorithm with Bee Pollinator has higher accuracy, higher level of stability, and the faster convergence speed.

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to trap into the local optimal. For overcoming these disadvantages of the k-means method, Flower Pollination Algorithm with Bee Pollinator is proposed. Discard pollen operator and crossover operator are applied to increase diversity of the population, and local searching ability is enhanced by using elite based mutation operator. Ten data sets are selected to evaluate the performance of proposed algorithm. Compared with DE, CS, ABC, PSO, FPA and k-Means, the experiment results show that Flower Pollination Algorithm with Bee Pollinator has not only higher accuracy but also higher level of stability. And the faster convergence speed can also be validated by statistical results.