Distributed Data Clustering Based on Flowers Pollination by Artificial Bees

This paper presents an unsupervised data clustering method based on flowers pollination by artificial bees we named it FPAB. FPAB does not require any parameter settings and any initial information such as the number of classes and the number of partitions on input data. Initially, in FPAB, bees move the pollens and pollinate them. Each pollen will grow in proportion to its garden flowers. Better growing will occur in better conditions. After some iteration natural selection reduces the pollens and flowers to form gardens of same type of flowers. The prototypes of each gardens are taken as the initial cluster centers for Fuzzy C Means algorithm which is used to reduce obvious misclassification errors. In the next stage the prototypes of gardens are assumed as a single flower and FPAB is applied to them again. Results from three small data sets show that the partitions produced by FPAB are competitive with those obtained from FCM or AntClass.