Personalised analytics is a powerful technology that can be used to improve the career, lifestyle, and health of individuals by providing them with an in-depth analysis of their characteristics as compared to other people. Existing research has often focused on mining general patterns or clusters, but without the facility for customisation to an individual’s needs. It is challenging to adapt such approaches to the personalised case, due to the high computational overhead they require for discovering patterns that are good across an entire dataset, rather than with respect to an individual. In this paper, we tackle the challenge of personalised pattern mining and propose a query-driven approach to mine objects with subspace similarity. Given a query object in a categorical dataset, our proposed algorithm, PRESS (Personalised Subspace Similarity), determines the top-k groups of objects, where each group has high similarity to the query for some particular subspace. We evaluate the efficiency and effectiveness of our approach on both synthetic and real datasets.

个性化分析是一项功能强大的技术，可通过向他人提供与他人相比的特征的深入分析来改善其职业，生活方式和健康状况。现有的研究通常集中在挖掘一般模式或集群，但没有针对个人需求进行定制的便利。由于将这些方法用于发现整个数据集（而不是针对个人）的良好模式所需的大量计算开销，因此使这些方法适应个性化案例具有挑战性。在本文中，我们解决了个性化模式挖掘的挑战，并提出了一种查询驱动的方法来挖掘具有子空间相似性的对象。给定分类数据集中的查询对象，我们提出的算法PRESS（PË ř sonalis Ë d小号ubspace小号imilarity），判定对象，其中每个组具有高相似度的查询一些特定子空间的前k个组。我们在合成数据集和实际数据集上都评估了我们的方法的效率和有效性。