Towards the Constraint Learning and Optimization Approach to Document Clustering

M. Rafi, Fizza Abid, Hamza Mustafa Khan, Anum Mirza
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Abstract

This research proposed autonomous constraint learning from a document collection to incorporate these constraints into an effective document clustering process. Constraint Clustering is based on semi-supervised approach towards document clustering, where some prior knowledge about the collection is readily available for clustering. The paper proposes algorithms based on sampling to find three different kind of constraints from the document collection (i) instance level (ii) cluster level and (iii) corpus level. The constraints integrated into constraint K-Mean produced multiple clusters satisfying the constraints. A boosting method is suggested to adaptively learn the constraint's priorities, constraint satisfaction criteria and optimal clustering solution from multiple possible solutions. The proposed algorithms are implemented and tested over standard text mining dataset. The evaluation measures for testing the algorithm involves purity, entropy, and F-measure. This experimental studies achieved encouraging results for constraint learning and on average, 6% improvements are achieved on clustering results.
文档聚类的约束学习与优化方法研究
本研究提出了基于文档集合的自主约束学习,将这些约束整合到有效的文档聚类过程中。约束聚类是基于文档聚类的半监督方法,其中关于集合的一些先验知识很容易用于聚类。本文提出了基于采样的算法,从文档集合(i)实例级(ii)聚类级和(iii)语料库级找到三种不同类型的约束。将约束整合到约束K-Mean中产生满足约束的多个聚类。提出了一种从多个可能解中自适应学习约束优先级、约束满足标准和最优聚类解的增强方法。在标准文本挖掘数据集上对算法进行了实现和测试。测试算法的评价指标包括纯度(purity)、熵(entropy)和F-measure。本实验研究在约束学习方面取得了令人鼓舞的结果,在聚类结果上平均提高了6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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