Web Mining Data Research Articles

Model based clustering of customer choice data

In several empirical applications analyzing customer-by-product choice data, it may be relevant to partition individuals having similar purchase behavior in homogeneous segments. Moreover, should individual- and/or product-specific covariates be available, their potential effects on the probability to choose certain products may be also investigated. A model for joint clustering of statistical units (customers) and variables (products) is proposed in a mixture modeling framework, and an appropriate EM-type algorithm for ML parameter estimation is presented. The model can be easily linked with similar proposals appeared in various contexts, such as co-clustering of gene expression data, clustering of words and documents in web-mining data analysis.

Spatio-Temporal Web Sensors Using Web Queries vs. Documents

In the Web world, one user creates and uploads a Web document about a phenomenon or event in the physical world, while another user retrieves and consumes Web documents by submitting a Web query. There have been many researches to mine Web documents in the exploding Web world, especially User Generated Content such as weblogs and microblogs, for knowledge about various phenomena and events in the physical world, and also Web services with the Web-mined knowledge have been made available for the public. However, there are few detailed investigations on how accurately Web-mined data reflect physical-world data. It must be socially-problematic to immoderately utilize the Web-mined data in public Web services without ensuring their accuracy sufficiently. Therefore, the previous papers introduced various Web Sensors to extract spatiotemporal data about a target phenomenon from Web documents searched by keyword(s) representing the target phenomenon, and tried to validate the potential and reliability of the Web-sensed spatiotemporal data. Moreover, this paper compares spatio-temporal Web Sensors analyzing Web queries and/or Web documents. 

Association Models for Web Mining

We describe how statistical association models and, specifically, graphical models, can be usefully employed to model web mining data. We describe some methodological problems related to the implementation of discrete graphical models for web mining data. In particular, we discuss model selection procedures.