文本聚類

Clustering is the typical numerical analysis which tries to group together like observations based on commonality or closeness of the observation data points.
In text analysis, we are almost repeating the same operation with clusters: trying to determine the relationships between word usage across a document. Text clustering refers to the task of identifying the clustering structure of a corpus of text documents and assigning documents to the identified cluster(s).
常用的方法：Two typical types of clustering algorithms, i.e., connectivity-based clustering (a.k.a., hierarchical clustering) and centroid-based clustering (e.g., k-means clustering).
k-meansclustering: reduces the sum of squares differences between relationships and group/cluster words where the distances are minimized to the thresholds specified, in this case, the number of clusters specified.

> library(stats)
> mymeans <- kmeans(dtm,5)
> mymeans

> freq <- findFreqTerms(dtm,10)

Last updated 5 years ago

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Clustering is the typical numerical analysis which tries to group together like observations based on commonality or closeness of the observation data points.
In text analysis, we are almost repeating the same operation with clusters: trying to determine the relationships between word usage across a document. Text clustering refers to the task of identifying the clustering structure of a corpus of text documents and assigning documents to the identified cluster(s).
常用的方法：Two typical types of clustering algorithms, i.e., connectivity-based clustering (a.k.a., hierarchical clustering) and centroid-based clustering (e.g., k-means clustering).
k-meansclustering: reduces the sum of squares differences between relationships and group/cluster words where the distances are minimized to the thresholds specified, in this case, the number of clusters specified.

> library(stats)
> mymeans <- kmeans(dtm,5)
> mymeans

> freq <- findFreqTerms(dtm,10)

Last updated 5 years ago

Was this helpful?