Cluster and outlier detection method based on multi-agent evolution

Abstract

The invention discloses a cluster and outlier detection method based on a multi-agent evolution, and mainly achieves that current traditional outlier detection algorithms can be used for detecting the outlier of high efficiency data cluster on data sets of different densities. The method comprises the steps of S1, initializing, S2, conducting K-means cluster algorithms to each intelligent agent, S3, calculating the energy of the intelligent agent, S4, performing a neighborhood competition operator, S5, performing a neighborhood crossover operator, S6, performing a mutation operator, S7, conducting K-means cluster algorithms, S8, conducting a self-learning operator, S9, updating a global optimization agent, S10, detecting the outlier, S11, obtaining a judgment result, S12, exporting outlier data, and S13, exporting data points with categories. The cluster and outlier detection method based on multi-agent evolution can effectively enhance the clustering efficiency and the outlier detection precision on different density data, reduce the calculation time, and be applicable to data sets of different densities.

Description

technical field [0001] The invention belongs to the technical field of computer data processing, and further relates to a clustering and outlier detection method based on multi-agent evolution in the technical field of data clustering and outlier detection. The invention adopts the technology of multi-agent evolution and local outlier point detection, which is beneficial to improve cluster division and outlier point detection precision. The invention can be used for clustering and outlier detection of natural data. Background technique [0002] Outlier detection and cluster analysis is an important research field in data mining and knowledge discovery. In practical applications, outlier detection is widely used in various fields, such as fraudulent credit card detection, intrusion detection, network and video surveillance, and weather forecast. The essence of the outlier is a phenomenon generated by a mechanism different from the general principle, and it is also called an...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the genetic algorithm is easy to fall into the local optimal situation, so that the final partition solution is not the optimal solution, and the stability is not high

This method uses the k-means clustering algorithm to cluster the data set. Although the k-means algorithm is relatively simple, the disadvantages of this method are that the efficiency of dividing data is not high and the convergence speed is slow; once a certain data If the points are considered outliers, they cannot be executed in the next generation, resulting in a decrease in the accuracy of the algorithm.

The disadvantage of this method is that in the algorithm, the parameters need to be set by experienced researchers, and the calculation amount is relatively high (N×N), which makes the algorithm take a long time to execute and the efficiency is not high

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