其中：“A”代表数据集中决策属性取值的集合，“C”代表某个类标号。

Here, "a" represents the set of decision attributes and their values, and "c" represents a kind of class label.

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半监督聚类通过利用少量有标号样本或成对约束等监督信息来提高聚类性能。

Semi-supervised clustering algorithms use a small amount of supervision information in the form of labeled data or pairwise constraints to improve clustering performance.

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聚类是通过相似度对没有类别标号的数据集中数据进行分组，使得组内对象相似度高而组间相似度低。

Clustering group the absence category labels data by similarity degree, there are high similarity inner group and low similarity between groups.

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聚类是通过相似度对没有类别标号的数据集中数据进行分组，使得组内对象相似度高而组间相似度低。

Clustering group the absence category labels data by similarity degree, there are high similarity inner group and low similarity between groups.

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