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\markright{(PRIP-2005 -- at Minsk, Belarus)}
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{\LARGE{\bf When a flower of iris is normal then are others abnormal?\\
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\hspace*{1in}\\
\hspace*{1in}\\
Akira Imada  \\
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Brest State Technical University \\ 
Belarus}
}
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%% What title means?
%% This is a topic In the context of pattern recognition/classification
%% above all 
%% intrusion detection ... our current concerne 
%% we need test data to test an intrusion-detector we are designing.
%% ----- motivation -----
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{\bf
In pattern recognition/classification \\
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above all
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ANOMALY (intrusion) DETECTION \\
 our current concerne \\
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we need
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test data to test an intrusion-detector we are designing.
}
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%%i'm quite a new to this topic
%% ----- motivation -----
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{\bf``When a flower of iris is normal then are others abnormal?''\\
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So many used \\
%% \hspace*{1in}\\ 
Fisher's Iris Dataset.\\
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But why IRIS? \\
Why not the other one? \\
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just saying  \\
``Because this is a widely used benchmark data.'' \\
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$\Downarrow$ \\
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Tautology?
}
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%% ----- for example
\clearpage
%%bibliography
{\tiny
%%\begin{description}
\begin{thebibliography}{99}
\bibitem{} Timmis et al.~(2000)
{\em An artificial immune system for data analysis.}
Biosystems, 55(1/3):143--150.

\bibitem{}  J.~Kim and P.~J.~Bentley (2001)
{\em Towards an Artificial Immune System for Network Intrusion Detection:
An Investigation of Clonal Selection with a Negative Selection Operator.}
Proceedings of the Congress on Evolutionary Computation.

\bibitem{}  Yu Guan
{\em K-means+: An Autonomous Clustering Algorithm.}

\bibitem{}  L. N. de Castro and J. Timmis
{\em Artificial Immune Systems: A Novel Paradigm to Pattern Recognition.}

\bibitem{}  I. Antoniou et al.~(1999)
{\em Information processing in Artificial Immune Systems: approaches and state of the art.}
Preprint of the International Solvay Institutes for Physics and Chemistry, SOLVAY 01-03, 
Brussels, 1999. 

\bibitem{}  A. Watkins et al.~(2004)
{\em Artificial immune recognition system (AIRS): Revisions and refinments.}
Proc. of the Society for the Study of Artificial Intelligence and the
Simulation of Behaviour (AISB) 2004 Symposium on the Immune System and cognition.
pp. 18--26.

\bibitem{}  E. Hart (2002)
{\em Immunology as a Metaphor for Computational Information Processing: Fact or Fiction ?}
Ph.D. Dissertation. Artifcial Intelligence Applications Institute,
Division of Informatics, University of Edinburgh

\bibitem{} D.~E.~Goodman, Jr et al.~(2001)
{\em An Investigation into the Source of Power for AIRS, an Artificial Immune Classification
System.}

\bibitem{}  J.~Mill et al.~(2003)
{\em An Application of Fuzzy Support Vectors.}

\bibitem{} D.~E.~Hershberger et al.~(2002)
{\em Distributed Multivariate Regression Using
Wavelet-based Collective Data Mining.}
%%\end{description}
\end{thebibliography}
}

%% details are not important here but notice so many report


%% ----- for example
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{\bf However, very impressive results are shown.\\
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e.g., Kim \&Bentley (2001) \\
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three different data sets 
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%%from this original data set \\
by taking one set as a SELF set and the rest as a NON-SELF set. \\
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A tenfold cross-validation method \\

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$\Downarrow$ \\
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the best TP rate reached 100\%  \\
the worst FP average rate was only 1\%.
}
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%% ----- for example
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{\bf tiny island in huge lake\\
needle in haystack\\
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or\\
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A random set of SELF and NON-SELF. (Ayara et al.)\\
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All points in the space should be representative of transaction.\\


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$\Downarrow$ \\
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We doubt IRIS is not such a case.
}
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%% --- (Figure) ---
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{\bf what does  iris data look like?}

%% --- (Figure) ---
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{\bf
\begin{table}[bhtp]
\begin{center}
%%\begin{footnotesize}
\begin{tabular}{c c c c c}                                        \hline \hline
5.1      &   3.5    &   1.4    &   0.2    &   setosa         \\
4.9      &   3.0    &   1.4    &   0.2    &   setosa         \\
4.7      &   3.2    &   1.3    &   0.2    &   setosa         \\
$\cdots$ & $\cdots$ & $\cdots$ & $\cdots$ & $\cdots$         \\
7.0      &   3.2    &   4.7    &   1.4    &   versicolor     \\
6.4      &   3.2    &   4.5    &   1.5    &   versicolor     \\
6.9      &   3.1    &   4.9    &   1.5    &   versicolor     \\
$\cdots$ & $\cdots$ & $\cdots$ & $\cdots$ & $\cdots$         \\
6.3      &   3.3    &   6.0    &   2.5    &   virginica      \\
5.8      &   2.7    &   5.1    &   1.9    &   virginica      \\
7.1      &   3.0    &   5.9    &   2.1    &   virginica      \\
$\cdots$ & $\cdots$ & $\cdots$ & $\cdots$ & $\cdots$         \\   \hline\hline
\end{tabular}
%%\end{footnotesize}
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\end{table}
}

%% --- (Figure) ---
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\noindent
{\bf $\Box$ Let's visualize the Iris Dataset of 4D --- Sammon Mapping}
\begin{figure}[hbtp]
 \begin{center}
   \epsfile{file=init-2d.eps,width=18cm,height=8cm}
 \end{center}
\end{figure}

%%what is sammon

%% --- (Figure) ---
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\noindent
{\bf $\Box$ A tiny-flat-island in a huge-lake}
\begin{figure}[hbtp]
 \begin{center}
   \epsfile{file=iris-evolution.eps,width=18cm,height=8cm}
 \end{center}
\end{figure}

\clearpage
\begin{figure}[hbtp]
 \begin{center}
   \epsfile{file=final-2d.eps,width=18cm,height=8cm}
 \end{center}
\end{figure}

----
%%KDD cup


%% --- (Figure) ---
\clearpage
in conclusion
if we know anomalcy should be in a known set
its really easy to detect.

another
training should be only by nomal




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