Mining Cluster Patterns in XML Corpora via Latent Topic Models of Content and Structure

摘要：

　　We present two innovative machine-learning approaches to topic model clustering for the XML domain.The first approach consists in exploiting consolidated clustering techniques,in order to partition the input XML documents by their meaning.This is captured through a new Bayesian probabilistic topic model,whose novelty is the incorporation of Dirichlet-multinomial distributions for both content and structure.In the second approach,a novel Bayesian probabilistic generative model of XML corpora seamlessly integrates the foresaid topic model with clustering.Both are conceived as interacting latent factors,that govern the wording of the input XML documents.Experiments over real-world benchmark XML corpora reveal the overcoming effectiveness of the devised approaches in comparison to several state-of-the-art competitors.

关键词： Bayesian probabilistic XML analysis XML clustering Latent topic modeling

作者: Gianni Costa Riccardo Ortale

作者单位: ICAR-CNR,Via P.Bucci 8/9C,Rende,CS,Italy

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 237-248

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）