Mining informative patterns from the Web, object-oriented and multiple data sources

挖掘多个不同数据集的能力将为更复杂的模式查询提供答案，以更有效的方式从历史数据中发现隐藏的、派生的、多媒体的和准确的知识，并向最终用户提供更好的建议。可以解决的一些示例查询是：(I)给定一组网页，获取它们在过去五年中具有最低价格范围的产品列表，(Ii)给定一组大学网页，按性别、级别和专业领域呈现教师，(Iii)从一组数据库中查找所有频繁购买的产品，包括它们的形象、客户评论、在一段时间内售出的数量。 因此，本研究的目标是：(1)使用面向对象的数据建模技术对Web数据等不同的数据集进行多数据库挖掘；(2)将模式挖掘技术集成到最终用户工具中，用于Web推荐、个人组织和系统监控。实现研究目标的科学方法包括使用非确定性有限状态自动机(NFA)来生成网站模式，以及它们之间的关系作为对象和类的层次结构。然后，利用关联规则和模式树挖掘技术，在众多挖掘技术中发展了面向对象的频繁模式挖掘技术，用于提取派生模式、稀有模式、频繁模式、相关模式等。这项拟议的研究大大减少了花费在网络搜索上的时间，并手动筛选隐藏的和错误的查询答案，从而有助于提高加拿大人的整体生活质量和生产力。它通过使模式挖掘过程更少的人工、更多的可用、更适用和更有效来提高组织和用户的生产力。

Ability to mine multiple diverse datasets will provide answers to more complex pattern queries, discover hidden, derived, multimedia and accurate knowledge from historical data, in a more efficient fashion, as well as provide better recommendations to end users. Some example queries that can be solved are: (i) given a set of web pages, get the list of products with lowest price ranges in them in the past five years, (ii) given a set of University web pages, present faculty by gender, ranks and area of specialization, (iii) find all frequently purchased products from a set of databases including their image, customer comments, number sold over a period of time. Thus, the objectives of the proposed research are to (1) build a set of techniques for multiple database mining of diverse datasets using object-oriented data modeling of such diverse data as web data, (2) integrate pattern mining techniques in end user tools for web recommendations, personal organization and system monitoring. Scientific methods for accomplishing research goals include using non-deterministic finite state automata (NFA) to generate web site schemas, and the relationships between them as object and class hierarchies. Then, use association rule and pattern tree mining techniques to develop object-oriented frequent pattern mining techniques among many other mining techniques for extracting derived, rare, frequent, relevant and other patterns. The proposed research contributes to overall quality of life of Canadians as well as their productivity by cutting down drastically, the amount of time spent on the web searching and sifting through hidden and incorrect answers to queries manually. It increases organizations' and users' productivity by making pattern mining process less manual, more available, more applicable and more effective.