Optimal clustering algorithm based on modified local outlier factor detection

Cluster analysis has been concerned by scholars at home and abroad in the field of unsupervised learning. Aiming at the disadvantages of K-means clustering algorithm for initial clustering center point sensitivity, poor data correlation in clusters and convergence to local optimization, an optimized clustering algorithm based on outlier factor is proposed in this paper. The algorithm firstly takes the information entropy weighted European distance as the basis of similarity measurement, in order to distinguish the difference between the data objects more obviously, then calculates the outlier factor of each data point by using the k distance parameter self-adjusting of the Local Outlier Factor algorithm and selects the candidate set of the initial clustering center, and finally optimizes the clustering center according to the outlier factor weighted distance method. The experimental results on UCI DataSet show that the accuracy of the optimization algorithm is higher than that of k-means++ algorithm, OFMMK-means algorithm and FCM algorithm, and its running speed is faster than the FCM algorithm. The algorithm can be better used in intrusion behavior detection, credit risk assessment and multi-fault diagnosis.