Clustering system, method, and program
AssigneeNEC Corporation
InventorsRyohei FUJIMAKI, Yousuke MOTOHASHI
Life of this patent
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Abstract
A classifier 81 classifies target data into a cluster on the basis of a mixture model defined using two different types of variables that indicate features of the target data. In this classification, the classifier 81 classifies the target data into a cluster on the basis of a mixture model in which a mixing ratio of the mixture model is represented by a function of a first variable and in which the element distribution of the clusters into which the target data is classified is represented by a function of a second variable.
Claims9
A clustering system comprising:
- a hardware including a processor;
- and
- a classifier, implemented by the processor, which classifies target data into a cluster based on a mixture model defined by using two different types of variables representing features of the target data,
- wherein the classifier classifies the target data into a cluster based on the mixture model in which a mixing ratio of the mixture model is represented by a function of a first variable and in which an element distribution of the cluster into which the target data is classified is represented by a function of a second variable.
A clustering method, wherein:
- a computer classifies target data into a cluster based on a mixture model defined by using two different types of variables representing features of the target data;
- and
- the computer classifies the target data into a cluster based on the mixture model in which a mixing ratio of the mixture mode is represented by a function of a first variable and in which an element distribution of the cluster into which the target data is classified is represented by a function of a second variable in the classification.
A non-transitory computer readable information recording medium storing a clustering program, when executed by a processor, which performs a method for:
- classifying target data into a cluster based on a mixture model defined by using two different types of variables representing features of the target data;
- and
- classifying the target data into a cluster based on the mixture mode in which a mixing ratio of the mixture model is represented by a function of a first variable and in which an element distribution of the cluster into which the target data is classified is represented by a function of a second variable.
Dependencies are inferred from the claim text.