密度峰值聚类算法(density peaks clustering algorithm,DPC)是2014年提出的一种新型聚类分析算法,它基于聚类中心局部密度大以及与密度更大点之间的距离较远两大特点绘制决策图寻找聚类中心,从而得到任意形状的簇.但在寻找聚类中心的...密度峰值聚类算法(density peaks clustering algorithm,DPC)是2014年提出的一种新型聚类分析算法,它基于聚类中心局部密度大以及与密度更大点之间的距离较远两大特点绘制决策图寻找聚类中心,从而得到任意形状的簇.但在寻找聚类中心的过程中,求解局部密度以及高密度距离属性都依赖于相似度矩阵的计算,计算复杂度较高,限制了密度峰值聚类算法在大规模数据集中的应用.针对此不足,提出基于网格筛选的密度峰值聚类算法(density peaks clustering algorithm based on grid screening,SDPC),根据数据的不均匀分布,使用网格化方法去除部分密度稀疏的点,然后再使用密度峰值聚类算法中决策图的方法选取聚类中心,可以在保证聚类准确性的基础上有效降低计算复杂度.理论分析和实验测试表明:基于网格筛选的密度峰值聚类算法不仅可以对大规模数据集进行正确的聚类,还极大地降低了计算复杂度.展开更多
针对基于密度的噪声应用空间聚类算法(density based spatial clustering of applications with noise,DBSCAN)计算复杂度较高以及无法聚类多密度数据集等问题,提出了一种网格聚类算法和DBSCAN相结合的融合聚类算法(G_FDBSCAN)。利用网...针对基于密度的噪声应用空间聚类算法(density based spatial clustering of applications with noise,DBSCAN)计算复杂度较高以及无法聚类多密度数据集等问题,提出了一种网格聚类算法和DBSCAN相结合的融合聚类算法(G_FDBSCAN)。利用网格划分技术将数据集划分为稀疏区域和密集区域,分而治之,降低计算的时间复杂度和采用全局参数引起的聚类误差;改进传统的DBSCAN聚算法得到FDBSCAN,将密集区域中网格聚类的结果作为一个整体参与后续的聚类,在网格划分基础上进行邻域检索,减少邻域检索和类扩展过程中对象的无效查询和重复查询,进一步减少时间开销。理论分析和实验测试表明,改进后的算法与DBSCAN算法、DPC算法、KMEANS算法、BIRCH算法和CBSCAN算法相比,在聚类结果接近或达到最优的情况下,聚类效率分别平均提升了24倍、11倍、2倍、3倍和1倍。展开更多
Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural...Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed,which works for both simple and complex questions.First,the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels,and named entities in questions are labeled by the conditional random field(CRF)model.Candidate entities are generated based on a dictionary,the disambiguation of candidate entities is realized based on predefined rules,and named entities mentioned in questions are linked to entities in knowledge base.Next,questions are classified into simple or complex questions by the machine learning method.Starting from the identified entities,for simple questions,one-hop relations are collected in the knowledge base as candidate relations;for complex questions,two-hop relations are collected as candidates.Finally,the multi-attention Bi-LSTM model is used to encode questions and candidate relations,compare their similarity,and return the candidate relation with the highest similarity as the result of relation linking.It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions,and the Bi-LSTM model with two attentions is adopted for complex questions.The experimental results show that,based on the effective entity linking method,the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions,which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.展开更多
针对密度峰值聚类(density peaks clustering,DPC)计算复杂度高的问题,提出一种面向密度峰值聚类的高效相似度度量(efficient similarity measure,ESM)法,通过仅度量最近邻之间的相似度构建不完全相似度矩阵。最近邻的选择基于一个随机...针对密度峰值聚类(density peaks clustering,DPC)计算复杂度高的问题,提出一种面向密度峰值聚类的高效相似度度量(efficient similarity measure,ESM)法,通过仅度量最近邻之间的相似度构建不完全相似度矩阵。最近邻的选择基于一个随机第三方数据对象,无需另外引入参数。基于ESM法构建相似度矩阵,提出一种改进的高效密度峰值聚类(efficient density peaks clustering,EDPC)算法,在保持准确率的同时提高DPC识别聚类中心的效率。理论分析和试验结果表明,ESM法通过减少一定不相似的相似度,可以有效提高DPC及其改进算法基于K最近邻的密度峰值聚类(density peaks clustering based on K-nearest neighbors,DPC-KNN)和模糊加权K最近邻密度峰值聚类(fuzzy weighted K-nearest neighbors density peaks clustering,FKNN-DPC)的计算效率,具有较强的可扩展性。展开更多
文摘密度峰值聚类算法(density peaks clustering algorithm,DPC)是2014年提出的一种新型聚类分析算法,它基于聚类中心局部密度大以及与密度更大点之间的距离较远两大特点绘制决策图寻找聚类中心,从而得到任意形状的簇.但在寻找聚类中心的过程中,求解局部密度以及高密度距离属性都依赖于相似度矩阵的计算,计算复杂度较高,限制了密度峰值聚类算法在大规模数据集中的应用.针对此不足,提出基于网格筛选的密度峰值聚类算法(density peaks clustering algorithm based on grid screening,SDPC),根据数据的不均匀分布,使用网格化方法去除部分密度稀疏的点,然后再使用密度峰值聚类算法中决策图的方法选取聚类中心,可以在保证聚类准确性的基础上有效降低计算复杂度.理论分析和实验测试表明:基于网格筛选的密度峰值聚类算法不仅可以对大规模数据集进行正确的聚类,还极大地降低了计算复杂度.
文摘针对基于密度的噪声应用空间聚类算法(density based spatial clustering of applications with noise,DBSCAN)计算复杂度较高以及无法聚类多密度数据集等问题,提出了一种网格聚类算法和DBSCAN相结合的融合聚类算法(G_FDBSCAN)。利用网格划分技术将数据集划分为稀疏区域和密集区域,分而治之,降低计算的时间复杂度和采用全局参数引起的聚类误差;改进传统的DBSCAN聚算法得到FDBSCAN,将密集区域中网格聚类的结果作为一个整体参与后续的聚类,在网格划分基础上进行邻域检索,减少邻域检索和类扩展过程中对象的无效查询和重复查询,进一步减少时间开销。理论分析和实验测试表明,改进后的算法与DBSCAN算法、DPC算法、KMEANS算法、BIRCH算法和CBSCAN算法相比,在聚类结果接近或达到最优的情况下,聚类效率分别平均提升了24倍、11倍、2倍、3倍和1倍。
基金The National Natural Science Foundation of China(No.61502095).
文摘Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed,which works for both simple and complex questions.First,the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels,and named entities in questions are labeled by the conditional random field(CRF)model.Candidate entities are generated based on a dictionary,the disambiguation of candidate entities is realized based on predefined rules,and named entities mentioned in questions are linked to entities in knowledge base.Next,questions are classified into simple or complex questions by the machine learning method.Starting from the identified entities,for simple questions,one-hop relations are collected in the knowledge base as candidate relations;for complex questions,two-hop relations are collected as candidates.Finally,the multi-attention Bi-LSTM model is used to encode questions and candidate relations,compare their similarity,and return the candidate relation with the highest similarity as the result of relation linking.It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions,and the Bi-LSTM model with two attentions is adopted for complex questions.The experimental results show that,based on the effective entity linking method,the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions,which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.
文摘针对密度峰值聚类(density peaks clustering,DPC)计算复杂度高的问题,提出一种面向密度峰值聚类的高效相似度度量(efficient similarity measure,ESM)法,通过仅度量最近邻之间的相似度构建不完全相似度矩阵。最近邻的选择基于一个随机第三方数据对象,无需另外引入参数。基于ESM法构建相似度矩阵,提出一种改进的高效密度峰值聚类(efficient density peaks clustering,EDPC)算法,在保持准确率的同时提高DPC识别聚类中心的效率。理论分析和试验结果表明,ESM法通过减少一定不相似的相似度,可以有效提高DPC及其改进算法基于K最近邻的密度峰值聚类(density peaks clustering based on K-nearest neighbors,DPC-KNN)和模糊加权K最近邻密度峰值聚类(fuzzy weighted K-nearest neighbors density peaks clustering,FKNN-DPC)的计算效率,具有较强的可扩展性。
