CAJ | 학술논문

云模型是云理论的核心，数据流聚类算法在云模型中有较好的应用前景，但也面临着聚类效率、聚类适应性的难题，为此本文提出了一种有抗体免疫的云模型数据流聚类算法。通过设置加权期望值、熵等参数形成云数据特有的数据概要结构，作为抗体代入人工免疫算法中；利用衰减函数和时刻权重来定量表现不同时刻的数据的重要性程度，并以抗体期望克服率为特征值以维持抗体的多样性，采用淘汰法确保最后的数据概要结构更符合云模型数据流的本质特征。实验表明，该算法在云模型数据流中的聚类处理速度和聚类效率均优于传统算法，具有一定的应用价值。
운모형시운이론적핵심，수거류취류산법재운모형중유교호적응용전경，단야면림착취류효솔、취류괄응성적난제，위차본문제출료일충유항체면역적운모형수거류취류산법。통과설치가권기망치、적등삼수형성운수거특유적수거개요결구，작위항체대입인공면역산법중；이용쇠감함수화시각권중래정량표현불동시각적수거적중요성정도，병이항체기망극복솔위특정치이유지항체적다양성，채용도태법학보최후적수거개요결구경부합운모형수거류적본질특정。실험표명，해산법재운모형수거류중적취류처리속도화취류효솔균우우전통산법，구유일정적응용개치。
There are good prospects for data stream clustering algorithm used for cloud model as the core of the cloud theory, which also faces the problem of clustering efficiency and clustering suitability, so that a data stream clustering algorithm used in cloud model based on artificial immune principle is proposed in this paper. Cloud data structure is set as antibody-generation of artificial immune algorithm which consists of weighted expectation, entropy and etc. The time instance weight and the attenuation function are used to behave the importance degree of the data of different moments, and antibody expectations overcome rate is made for characteristics value to maintain diversity of antibodies. At last the elimination method is used to ensure that the last synopsis data structure according for the essential characteristics of cloud data stream as more as possible. The experiments show that the algorithm is better than traditional algorithm in both the clustering speed and clustering efficiency and has a certain value.