计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
20期
120-125
,共6页
K-means算法%聚类%对称距离%方向约束
K-means算法%聚類%對稱距離%方嚮約束
K-means산법%취류%대칭거리%방향약속
K-means algorithm%clustering%symmetry distance%direction constraint
K-means算法是数据挖掘领域研究、应用都非常广泛的一种聚类算法,其各种衍生算法很多,其中包括近年出现的以点对称距离为测度的K-means聚类算法。在点对称距离聚类算法的基础上提出一种新的聚类算法,根据对对称性的分析,为对称性的描述增加方向约束,提高对称距离的描述准确性,以此来提高聚类的准确性。同时,针对对称点成对出现的特点,调整了聚类过程中的收敛策略,以对称点对连线中点计算聚类中心,改善了基于对称距离的聚类算法收敛性能。通过数值仿真比较了所提算法与原有算法的优劣,结果显示该算法在计算复杂度不变的条件下获得了更准确的结果,聚类结果更接近数据的真实分类。
K-means算法是數據挖掘領域研究、應用都非常廣汎的一種聚類算法,其各種衍生算法很多,其中包括近年齣現的以點對稱距離為測度的K-means聚類算法。在點對稱距離聚類算法的基礎上提齣一種新的聚類算法,根據對對稱性的分析,為對稱性的描述增加方嚮約束,提高對稱距離的描述準確性,以此來提高聚類的準確性。同時,針對對稱點成對齣現的特點,調整瞭聚類過程中的收斂策略,以對稱點對連線中點計算聚類中心,改善瞭基于對稱距離的聚類算法收斂性能。通過數值倣真比較瞭所提算法與原有算法的優劣,結果顯示該算法在計算複雜度不變的條件下穫得瞭更準確的結果,聚類結果更接近數據的真實分類。
K-means산법시수거알굴영역연구、응용도비상엄범적일충취류산법,기각충연생산법흔다,기중포괄근년출현적이점대칭거리위측도적K-means취류산법。재점대칭거리취류산법적기출상제출일충신적취류산법,근거대대칭성적분석,위대칭성적묘술증가방향약속,제고대칭거리적묘술준학성,이차래제고취류적준학성。동시,침대대칭점성대출현적특점,조정료취류과정중적수렴책략,이대칭점대련선중점계산취류중심,개선료기우대칭거리적취류산법수렴성능。통과수치방진비교료소제산법여원유산법적우렬,결과현시해산법재계산복잡도불변적조건하획득료경준학적결과,취류결과경접근수거적진실분류。
K-means is a well studied and widely used clustering algorithm in data mining. There are many clustering algo-rithms evolved from K-means. For example, the symmetry-based version of the K-means algorithm using the point sym-metry distance as the similarity measure is proposed at recent years. In this paper, a new clustering algorithm based on point symmetry distance clustering algorithm is proposed. The direction constraint is put forward after studying the pro-perties of symmetry to enhance the description of symmetric distance and improve the accuracy of clustering. For the fact that symmetry is the relationship between two points, the strategy of convergence is modified to use the midpoint of the symmetry pair to calculate the cluster centers. The convergence performance of clustering is improved. By numerical simu-lation it shows that the proposed algorithm reaches a more accurate result with the same computational complexity as the existing one.