Inteligent Tag Grouping By Using An Aglomerative Clustering Algorithm
Date Issued
2013
Author(s)
Gajduk, Andrej
Madjarov, Gjorgji
Gjorgjevikj, Dejan
Abstract
Tagging can be defined as a process of assigning short textual
descriptions or key-words (called tags) to information objects.
It is a simple approach to information organization that was
regularly practiced over the last years. Tagging systems
usually have relatively flat tags. This means that while one
can easily browse by a tag, one cannot as easily see tags that
have wider or more specific meaning than a given tag. It is
also difficult to get a broad overview of the tags that do exist
in the tagging systems, aside from frequency based displays
like tag clouds. In this paper we investigate how correlated
tags can be grouped by using an agglomerative clustering
algorithm considering only the label part (output space) of the
data. We have applied this approach on the StackOverflow
tag cloud and discuss the obtained results.
descriptions or key-words (called tags) to information objects.
It is a simple approach to information organization that was
regularly practiced over the last years. Tagging systems
usually have relatively flat tags. This means that while one
can easily browse by a tag, one cannot as easily see tags that
have wider or more specific meaning than a given tag. It is
also difficult to get a broad overview of the tags that do exist
in the tagging systems, aside from frequency based displays
like tag clouds. In this paper we investigate how correlated
tags can be grouped by using an agglomerative clustering
algorithm considering only the label part (output space) of the
data. We have applied this approach on the StackOverflow
tag cloud and discuss the obtained results.
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