计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
17期
124-127,144
,共5页
入侵检测%数据挖掘%聚类算法%K-means聚类%最小支撑树
入侵檢測%數據挖掘%聚類算法%K-means聚類%最小支撐樹
입침검측%수거알굴%취류산법%K-means취류%최소지탱수
intrusion detection%data mining%clustering algorithm%K-means clustering%minimum spanning tree
为了弥补传统K-means聚类算法在K值确定和初始中心选择难等方面的不足，基于“合并与分裂”思想，提出一种改进的K-means聚类算法。将数据独立程度概念引入实验数据子集构造理论中，利用独立程度评价属性的重要性；根据点密度将数据集合并为若干类，结合最小支撑树聚类算法与传统K-means聚类算法实现分裂；使用KDD Cup99数据集对改进算法在入侵检测中的应用进行仿真实验。结果表明，改进算法在检测率和误报率方面均优于传统K-means算法。
為瞭瀰補傳統K-means聚類算法在K值確定和初始中心選擇難等方麵的不足，基于“閤併與分裂”思想，提齣一種改進的K-means聚類算法。將數據獨立程度概唸引入實驗數據子集構造理論中，利用獨立程度評價屬性的重要性；根據點密度將數據集閤併為若榦類，結閤最小支撐樹聚類算法與傳統K-means聚類算法實現分裂；使用KDD Cup99數據集對改進算法在入侵檢測中的應用進行倣真實驗。結果錶明，改進算法在檢測率和誤報率方麵均優于傳統K-means算法。
위료미보전통K-means취류산법재K치학정화초시중심선택난등방면적불족，기우“합병여분렬”사상，제출일충개진적K-means취류산법。장수거독립정도개념인입실험수거자집구조이론중，이용독립정도평개속성적중요성；근거점밀도장수거집합병위약간류，결합최소지탱수취류산법여전통K-means취류산법실현분렬；사용KDD Cup99수거집대개진산법재입침검측중적응용진행방진실험。결과표명，개진산법재검측솔화오보솔방면균우우전통K-means산법。
An improved K-means clustering algorithm is put forward on basis of the split-merge method for the purpose of remedying defects both in determination of value in K and in selection of initial cluster centre of traditional K-means clustering. The concept of independence degree of date is incorporated into the experimental date subset construction theory, using independence degree to evaluate the importance of nature. The database is merged into several classes in respect of density of date points, the combination of the minimum spanning tree algorithm and traditional K-means clustering algo-rithm is conducive to the achievement of splitting. The KDD Cup99 database is applied to conduct simulation experiment on the application of the improved algorithm in intrusion detection. The results indicate that the improved algorithm pre-vails over traditional K-means algorithm in detection rate and false alarm rate.
