本文選題：K-調(diào)和均值 + 聚類(lèi)��； 參考：《計(jì)算機(jī)應(yīng)用與軟件》2016年11期

【摘要】：針對(duì)K-調(diào)和均值算法中距離度量將所有屬性視為相等重要而存在的不足,提出一種利用自動(dòng)屬性加權(quán)的改進(jìn)聚類(lèi)算法。在算法的目標(biāo)函數(shù)中,用加權(quán)歐氏距離替代傳統(tǒng)的歐氏距離,并證明了使得算法能夠收斂的屬性權(quán)重更新機(jī)制。為進(jìn)一步提高聚類(lèi)性能,將粒子群算法融入到改進(jìn)的屬性加權(quán)聚類(lèi)算法中以抑制其陷于局部最優(yōu),其中采用聚類(lèi)中心和屬性權(quán)重的值同時(shí)表示粒子的位置進(jìn)行尋優(yōu)。在UCI數(shù)據(jù)集的測(cè)試結(jié)果表明,該算法的聚類(lèi)指標(biāo)平均提高了約9個(gè)百分點(diǎn),具有更高的聚類(lèi)準(zhǔn)確性和穩(wěn)定性。
[Abstract]:An improved clustering algorithm based on automatic attribute weighting is proposed to overcome the disadvantage that the distance measure in the K- harmonic mean algorithm considers all attributes to be equal. In the objective function of the algorithm, the traditional Euclidean distance is replaced by the weighted Euclidean distance, and the attribute weight updating mechanism that makes the algorithm convergent is proved. In order to further improve the clustering performance, the particle swarm optimization (PSO) algorithm is incorporated into the improved attribute weighted clustering algorithm to restrain it from falling into the local optimum, in which the values of the clustering center and the attribute weight are used to represent the location of the particles for optimization at the same time. The test results in UCI data set show that the clustering index of the algorithm increases by about 9 percentage points on average and has higher clustering accuracy and stability.
【作者單位】： 江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院;
【基金】：江蘇省自然科學(xué)基金項(xiàng)目(BK20140165)
【分類(lèi)號(hào)】：TP311.13

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