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% Document Info
\author{perascotta}
\pdfinfo{
  /Title (data-mining-2.pdf)
  /Creator (Cheatography)
  /Author (perascotta)
  /Subject (Data Mining 2 Cheat Sheet)
}

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% Header and Footer
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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
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    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Data Mining 2 Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{perascotta} via \textcolor{DarkBackground}{\uline{cheatography.com/21031/cs/3877/}}}
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\fancyfoot[L]{ \footnotesize
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}perascotta \\
  \uline{cheatography.com/perascotta} \\
  \end{tabulary}
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\begin{tabulary}{5.8cm}{L}
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 13th May, 2016.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
\columnbreak
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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% www.emerson.emory.edu/services/latex/latex_169.html
\footnotesize % Small font.

\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Sequential Patterns}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{{\bf{Occorrenze}} : \textless{}\{A,B\}\textgreater{} \textless{}\{A,C\}\textgreater{} ... \newline - min\_gap = 1 -{}-\textgreater{} distanze \textgreater{} di uno: \textless{}2,3\textgreater{} NO \newline - max\_gap = 2 -{}-\textgreater{} distanze \textless{}= a 2: \textless{}2,5\textgreater{} NO \newline - max\_span = 3 -{}-\textgreater{} distanza \textless{}= a 3: \textless{}2,3,5\textgreater{} NO \newline {\bf{GSP}}: ab -{}-\textgreater{} a -{}-\textgreater{} b \newline 1 - Genero gli 1-itemset e valuto il supporto; \newline 2 - Genero tutte le sequenze dagli itemset validi e valuto il supporto; \newline 3 - Genero le 3-sequenze dal passo due, valuto se per ogni 3sequenza, togliendo uno alla volta un membro, ha supporto \textgreater{} sup\_min, se si la tengo altrimenti la elimino. Successivamente valuto il supporto delle 3-sequenze rimaste; \newline 4 - Continuo come al passo 3: genero, pruning, valuto supporto.} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Serie temporali}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{{\bf{KNN}}: distanza euclidea tra una serie e tutte quelle del training e prendo i k più bassi. \newline {\bf{DTW}}: matrici enormi. Scelgo quella con vertice altro-destra minore e seleziono il cammino con minor costo. La matrice scelta determina la classe.} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Classificazione}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{{\bf{KNN}}: scelgo i k-vicini con minor distanza \newline   {\emph{Dati continui}}: distanza euclinea; \newline   {\emph{Dati categorici}}: \newline      1) matrici attributi/classi; \newline      2) {\emph{d(alto|basso)}}=|(alti classe SI/tot alti)-(bassi classe SI/tot bassi)|+|stessa cosa per classe NO|; \newline      3) somma distanze al quadrato. \newline {\bf{Decision Tree}}: \newline   a) Controllare se le regole possono essere semplificate (magari un solo attributo a sinistra) attraverso il {\emph{PE = (Training\_ERROR + 0.5)/N}}. La regola con PE minore è quella migliore; \newline   b) Costruire le matrici di confusione per i modelli e valutare statistiche sia sul training che sul test set: \newline      o\textbackslash{}c   |YES  NO | Accuracy: (TP+TN)/tutti \newline      YES |  TP   FN  |  Precision: TP\textbackslash{}(TP+FP) \newline       NO   |  FP   TN | Recall: TP\textbackslash{}(TP+FN) \newline {\bf{Bayesian Model}}: scelgo la classe dell'elemento con P maggiore \newline   {\emph{Dati continui}}: media, varianza, funzione (per entrambe le classi); \newline   {\emph{Dati categorici}}: Produttoria di P(X|YES){\emph{P(YES) \newline    dove P(X|YES) = (n.con X)\textbackslash{}(n.tot YES) \newline    alla fine: matrice di confusione e statistiche. \newline   }}Dati continui E categorici*: come i casi precedenti e moltiplicazione tra le varie probabilità.} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}