Cross-relational clustering with user's guidance

Clustering is an essential data mining task with numerous applications. However, data in most real-life applications are high-dimensional in nature, and the related information often spreads across multiple relations. To ensure effective and efficient high-dimensional, cross-relational clustering, we propose a new approach, called CrossClus, which performs cross-relational clustering with user’s guidance. We believe that user’s guidance, even likely in very simple forms, could be essential for effective high-dimensional clustering since a user knows well the application requirements and data semantics. CrossClus is carried out as follows: a user specifies a clustering task and selects one or a small set of features pertinent to the task. CrossClus extracts the set of highly relevant features in multiple relations connected via linkages defined in the database schema, evaluates their effectiveness based on user’s guidance, and identifies interesting clusters that fit user�...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Xiaoxin Yin, Jiawei Han, Philip S. Yu
• Keywords: Cross-relational Clustering, Data Mining, KDD 2005, Multiple Relations, User’s Guidance