Aiming at multi-scale representation of spatial data not supported by R-tree and the objects in the same rank are not-clustered, a multi-scale index structure of spatial data based on cluster algorithm is proposed. Hi...
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Aiming at multi-scale representation of spatial data not supported by R-tree and the objects in the same rank are not-clustered, a multi-scale index structure of spatial data based on cluster algorithm is proposed. Hierarchical structure of index tree is made use of reflecting the change of resolution ratio. The spatial objects of the same rank are divided into different groups using k-means cluster algorithm, decreasing the region covering and region overlap. The result of our experiments shows that the algorithm, compared with other ways, has a distinct superiority in the speed of multi-scale display of spatial data.
Many kinds of huge amount of tweets about realworld events are generated everyday in ***,the disorganization messages required to be classified by topics and events are one of challenges to get knowledge *** solve the...
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ISBN:
(纸本)9781479941681
Many kinds of huge amount of tweets about realworld events are generated everyday in ***,the disorganization messages required to be classified by topics and events are one of challenges to get knowledge *** solve the problem,we propose a novel method that combines the cluster algorithm with label propagation algorithm to detect topics in ***,we use canopy cluster algorithm to cluster tweets,canopy cluster algorithm could divides a tweet into different clusters,and the tweet which only belongs to one cluster will be ***,the mechanism of label propagation is used to label the tweets that in the overlapping of different *** order to evaluate our algorithm,we use two baseline algorithms,LDA (Latent Dirichlet Allocation) and Single-Pass cluster *** apply three algorithms on tweet dataset with three topics and some noisy data,and experiment results show our method outperforms other algorithms on precision and recall rate.
Many kinds of huge amount of tweets about realworld events are generated everyday in Twitter. However, the disorganization messages required to be classified by topics and events are one of challenges to get knowledge...
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Many kinds of huge amount of tweets about realworld events are generated everyday in Twitter. However, the disorganization messages required to be classified by topics and events are one of challenges to get knowledge effectively. To solve the problem, we propose a novel method that combines the cluster algorithm with label propagation algorithm to detect topics in twitter. First, we use canopy cluster algorithm to cluster tweets, canopy cluster algorithm could divides a tweet into different clusters, and the tweet which only belongs to one cluster will be labeled. Second, the mechanism of label propagation is used to label the tweets that in the overlapping of different clusters. In order to evaluate our algorithm, we use two baseline algorithms, LDA(Latent Dirichlet Allocation) and Single-Pass cluster algorithm. We apply three algorithms on tweet dataset with three topics and some noisy data, and experiment results show our method outperforms other algorithms on precision and recall rate.
In the incremental learning process of Support Vector Machines,the Non-support vectors which is close to support vector samples are discarded in tradition *** it is likely to change into the Support Vector after addin...
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In the incremental learning process of Support Vector Machines,the Non-support vectors which is close to support vector samples are discarded in tradition *** it is likely to change into the Support Vector after adding new training *** resolve this problem,this paper proposes a new method that combines Support Vector Machine with clustering *** this method,firstly,use clustering algorithm to cluster the training sample set and get clustering particles;secondly,look all centers of clustering particles as new samples training set and reconstruct the training samples set;then,train the new training samples set with Fuzzy Support Vector Machine（FSVM） and obtain the support vectors,and discard the samples that satisfy KKT conditions,put the samples that don not meet the KKT conditions and the support vectors together to reconstitute a new training set,train them *** results show that this method can enhance the classification accuracy rate and improve the speed of SVM training and classification speed,as keeping the generalization ability of SVM incremental learning.
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