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来源：百度知道 编辑：UC知道 时间：2024/05/17 02:57:53
基于k-means算法的心理数据异常分析

摘要：随着社会的进步和发展，人的心理对于人的生活和工作的影响越来越大。本课题主要的研究内容是利用离群挖掘技术找出心理异常的心理个案，提供决策支持方案。本设计采用的挖掘方法是聚类算法中的k-means算法，这种算法的主要原理是：接受输入量心理数据集合s和类数目k，然后将s个数据对象划分为k个类，最后计算出每个对象至各自中心点的距离并按降序排列出来，距离越大的就是越接近异常的。这样就可以从众多的心理数据中挖掘出心理异常的部分。可以让决策者通过分析结果快速地采取措施和采用预防或治疗的方法。从而达到心理治疗的理想效果，预防或者减免因心理异常而发生的意外事故。
关键词：心理；异常；离群挖掘；k-means算法；分析

K-means algorithm based on the psychological analysis of the data anomaly

Abstract: With the social progress and development of the psychological life of the people and the growing impact of the work. The main research topic is the use of outlier mining techniques to identify cases of psychological abnormality of psychological, providing decision support program. Mining of this design method is used in the clustering algorithm k-means algorithm, the main principle of this algorithm is: accept the input data set of psychological type s and the number of k, then the object s data is divided into k-category, and finally each object to calculate the distance to each point and come out in descending order, the distance is greater the closer abnormal. This can be from a wide range of psychological data mining part of a psychological abnormality. Allows decision-makers through the analysis of the results of the rapid adoption of measures and methods of prevention or treatment.