Abstract: Aiming at the problems of fuzzy C-means (FCM) clustering algorithm that does not consider the importance of different attributes of samples and neighborhood information, a FCM algorithm based on entropy and neighborhood constraints is proposed. First the entropy value of each attribute of the sample is calculated to give weight to each attribute, the attribute weight is combined to improve the distance measurement function; then the neighborhood membership weight is calculated according to the distance between the neighborhood sample and the center sample, and the neighborhood membership is got by weighting. The membership degree of the neighborhood constrains the objective function, and the iterative process of the degree of membership is modified, finally the purpose of improving the performance of the FCM clustering algorithm is achieved. Theoretical analysis and experimental results on artificial data sets and multiple UCI data sets show that the improved algorithm is superior to the traditional FCM algorithm, PCM algorithm, KFCM algorithm, KPCM algorithm, and DSFCM algorithm in terms of clustering effect and robustness, which shows the effectiveness of this algorithm.

Key words: fuzzy C-means algorithm, clustering algorithm, neighborhood information, entropy weight method