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\input{../../generic-preamble/generic-preamble}
\usepackage{../clrscode}
\usepackage{url}
\begin{document}

\title{Clustering Algorithms for Data Mining: An Overview}
\makeatletter
\def\@author{Dan Ports and Sarah Lieberman\\\texttt{\{drkp,sarahl\}@mit.edu}}
\makeatother
\maketitle


\begin{abstract}
  We provide a survey of current clustering algorithms as they might
  be applied toward data mining. We begin by presenting a list of
  criteria for evaluating clustering algorithms, and then consider
  each of the major classes of clustering algorithms and a few
  representative algorithms from each. In particular, hierarchical,
  partitioning, density-based, and grid-based algorithms are
  discussed. The strengths and weaknesses of each type of algorithm
  are evaluated, and we provide some indications of which types of
  data sets each algorithm is best suited for.
\end{abstract} 

\tableofcontents

\input{intro}
\input{hierarchical}
\input{partitioning}
\input{density}
\input{grid}
\input{conclusion}

\nocite{survey}
\nocite{evaluating}
\nocite{clustering-streams}

\bibliographystyle{abbrv}
\bibliography{paper}
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