CAJ | 학술논문

在社会网络中，标签聚类研究可以解决标签冗余和语义模糊等问题。为了提高聚类有效性，提出综合标签共现信息确定标签特征向量，通过特征向量的提取计算相似度，将传统聚类算法中用几何距离计算对象与中心对象的距离改为用皮尔森相关系数计算，提出结合K-means聚类算法对标签进行聚类的标签共现聚类算法，并分析了算法的复杂度。最后对不同聚类算法进行了相关对比实验，实验结果表明该聚类算法效果要好于其他的聚类算法，从而验证了该聚类算法的有效性和可行性。
재사회망락중，표첨취류연구가이해결표첨용여화어의모호등문제。위료제고취류유효성，제출종합표첨공현신식학정표첨특정향량，통과특정향량적제취계산상사도，장전통취류산법중용궤하거리계산대상여중심대상적거리개위용피이삼상관계수계산，제출결합K-means취류산법대표첨진행취류적표첨공현취류산법，병분석료산법적복잡도。최후대불동취류산법진행료상관대비실험，실험결과표명해취류산법효과요호우기타적취류산법，종이험증료해취류산법적유효성화가행성。
In the social network, tag clustering analysis can deal with problems such as tag redundancy and semantic fuzzi-ness and so on. In order to improve the effectiveness of clustering, it proposes to integrate label co-occurrence information and derive the feature vector of label, extracts the feature vector to calculate the similarity. The traditional clustering algo-rithm uses the geometric distance to calculate the distance to the object and the center of the object, now uses the Pearson correlation coefficient to calculate. The tag clustering algorithm that combines with K-means clustering algorithm to clus-ter label is proposed, and then analyzes the complexity of the algorithm. Finally, doing relevant comparative experiments for different clustering algorithms, the experimental results show that the proposed clustering algorithm enhances the clus-tering performance than other clustering algorithms, and verify the availability and effectiveness of the proposed cluster-ing algorithm.