By the same authors

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

Research output: Contribution to conferencePaper

Author(s)

Department/unit(s)

Publication details

DatePublished - 2007
Original languageUndefined/Unknown

Abstract

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 features considered in clustering on the classification
performance is also evaluated.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations