实例级约束主动学习提升半监督文档聚类效率

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在当前的IT领域，半监督文档聚类作为一种高效的数据分析技术，因其在有限监督数据的条件下对非标记文档进行自动分类而备受关注。这种技术尤其适用于那些数据标记成本高昂的场景，因为通过最小化人工标注的需求，能够最大化信息的利用，从而提升聚类效果。本文主要介绍了一种创新的半监督文档聚类算法，名为Cons-DBSCAN（约束DBSCAN）。DBSCAN是一种经典的密度聚类算法，但在此基础上，作者引入了实例级约束的概念，以指导聚类过程。实例级约束指的是针对每个文档个体的具体信息，它能帮助算法更精确地识别哪些文档属于同一类别，哪些应该被分隔开。这种方法使得Cons-DBSCAN能够更好地处理复杂的数据分布，并提高聚类的准确性和稳定性。此外，文章还探讨了主动学习在这一过程中的应用。主动学习是一种机器学习策略，它允许系统根据现有知识选择最有价值的信息样本进行标记，以提升模型的学习效率。在文档聚类的背景下，这意味着算法会选择那些对于当前聚类决策最有影响力或者能带来最大信息增益的文档，请求用户对其进行标记，进一步优化聚类结果。实验部分显示，Cons-DBSCAN算法结合主动学习方法，即使在监督数据相对匮乏的情况下，也能显著提升聚类性能。这证明了通过智能地选择和利用信息性实例级别的约束，可以在节省人力成本的同时，有效提升半监督文档聚类的准确性。这篇研究提供了一个新颖且实用的半监督文档聚类框架，利用主动学习策略和实例级约束，不仅优化了传统的DBSCAN，而且在实际应用中展现出强大的性能优势。这对于数据密集型的现代企业来说，无疑是一个重要的工具，有助于他们从大量未标记数据中挖掘有价值的信息，降低成本，提高业务洞察力。

Effective semi-supervised document clustering 571

clustering algorithm and of computing the similarity between documents. Section 4 presents

our semi-supervised document clustering algorithm. In Sect. 5,wedescribeamethodfor

actively picking good constraints by asking queries of the form “Are these two examples in

same or different classes?”. Section 6 discusses the experimental results on several document

datasets. Finally, we present our conclusions and future work in Sect. 7.

2 Related work

2.1 Semi-supervised clustering

Existing methods for semi-supervised clustering fall into two general approaches: constraint-

based methods and metric-based methods. In constraint-based approaches, the clustering

algorithm itself is modiﬁed so that user-provided labels or constraints are used to get a more

appropriate clustering. Wagstaff et al. [11] enforced constraints to be satisﬁed during the

cluster assignment in the clustering process. Basu et al. [12] proposed two semi-supervised

variants of k-means clustering which use labeled data to generate initial seed clusters and

guide the clustering process. Davidson and Ravi [13] presented the feasibility of clustering

under different types of constraints. And a constrained version of the k-means algorithm was

developed in their paper.

In metric-based approaches, an existing clustering algorithm that used a particular distor-

tion measure is employed; however, the measure is ﬁrst trained to satisfy the labels or con-

straints in the supervised data. Several typical algorithms have been proposed in this area.

Xing et al. [14] combined the Newton Raphson method and iterative projection together

to learn a Mahalanobis distance for k-means clustering. Screen proposed by Tang et al.

[15] ﬁrstly used the constraint-guided feature projection to reduce the dimensionality, which

is equivalent to learn a metric, and then applied the metric into the constrained spherical

k-means algorithm. Bar-Hillel et al. [16] presented an approach to learn a global Mahalan-

obis metric using relevant component analysis (RCA) algorithm. Chang and Yeung [17]

proposed a metric learning method, which performs non-linear transformation globally and

linear transformation locally. Kumar et al. [18] learned the underlying dissimilarity measure

while ﬁnding compact clusters in the given dataset. Yan and Domeniconi [19] addressed

the high-dimensionality problem by projecting the data and constraints in multiple subspac-

es and learning positive semi-definite similarity matrices therein. Hu et al. [20]integrated

the constraints into the k-means objective function, which is expressed as equivalent trace

formulation. Then, the objective function was optimized by eigen-decomposition.

Most existing semi-supervised clustering algorithms are k-means like methods, which are

not suitable for discovering clusters with non-convex shapes. Moreover, it needs to know the

real number of clusters in dataset, which is often not known in advance.

2.2 Active learning

Active learning in classiﬁcation is a long-studied problem, where different principles of sam-

ple selection have been studied. Some approaches choose most uncertain samples, which are

closest to the current classiﬁcation boundary [21]. Some methods select examples which

reduce the size of the version space [22]. Some methods choose the samples so that the

generalization error is minimized [23,24]. Some other approaches deal with active learning

in the feature level [25,26].

There has been little work on investigating active learning for semi-supervised docu-

ment clustering. Basu et al. [27] proposed a Pairwise Constrained K-Means (PCK-Means)

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