Clustering Approach to Generalized Pattern Identification Based on Multi-instanced Objects with DARA

Clustering is an essential data mining task with various types of applications. Traditional clustering algorithms are based on a vector space model representation. A relational database system often contains multirelational information spread across multiple relations (tables). In order to cluster such data, one would require to restrict the analysis to a single representation, or to construct a feature space comprising all possible representations from the data stored in multiple tables. In this paper, we present a data summarization approach, borrowed from the Information Retrieval theory, to clustering in multi-relational environment. We find that the data summarization technique can be used here to capture the typical high volume of multiple instances and numerous forms of patterns. Our experiments demonstrate a technique to cluster data in a multi-relational environment and show the evaluation results on the mutagenesis dataset. In addition, the effect of varying the number of fea...

• Type: Conference
• Year: 2007
• Where: ADBIS
• Authors: Rayner Alfred, Dimitar Kazakov
• Keywords: ADBIS 2007, Data Summarization, Data Summarization Technique, Database, Traditional Clustering Algorithms