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\title{Mineração de Dados Aplicada}
\subtitle{The Pattern Discovery Process}

\author{Loïc Cerf}

\date{March, 12th 2025}

\institute
{
  \scriptsize
  DCC -- ICEx -- UFMG
  \bigskip\\
  \includegraphics[width=.334113288\linewidth]{images/dcc}\hfill\includegraphics[width=.5\linewidth]{images/ufmg}
}

\subject{The Pattern Discovery Process}

\keywords{Data mining, process, data structure}

\hypersetup{pdfpagemode=FullScreen}

\AtBeginSection[]
{
  \begin{frame}<beamer>
    \frametitle{Outline}
    \tableofcontents[currentsection]
  \end{frame}
}

\begin{document}

\begin{frame}[plain]
  \titlepage
\end{frame}

\section*{Practical matters}

\begin{frame}
  \frametitle{\texttt{hello, world}}
  I am:
  \begin{itemize}
  \item Loïc Cerf;
  \item French;
  \item Still learning Portuguese;
  \item \alert<2>{Your teacher for this course;}
  % \item A free software advocate.
  \item \alert<3>{A free software advocate.}
  \end{itemize}

  \vfill
  % \pause

  % The Web page of the course, which hosts these slides, is
  % \url{http://dcc.ufmg.br/~lcerf/pt/mda.html}.

  \uncover<2->{\only<-2>{The Web page of the course, which hosts these
      slides, is
      \url{http://dcc.ufmg.br/~lcerf/pt/mda.html}}\only<3>{Saturday 26
      April, participate to FLISoL: install party (room 2013) and
      talks (room 2077) about free software.  It is gratis and the
      call for talks is open until April 6th:
      \url{http://bh.flisol.org.br}}}
\end{frame}

\begin{frame}
  \frametitle{Organization of the course}
  During the sessions, there will be:
  \begin{itemize}
  \item ``Theory'';
  \item Practice;
  \item Your (inter)active implication.
  \end{itemize}

  \vfill
  \pause

  Following the ICEx rule, a student who misses more than 25\% of the
  course fails it.

  \vfill
  \pause

  The subject is \emph{elective} but, to pass it, the work is
  \emph{compulsory}!
\end{frame}

\begin{frame}
  \frametitle{``Theory''}
  The ``theory'' will:
  \begin{itemize}
  \item mainly give ``big pictures'';
  \item decrease (in volume) along the sessions;
  \item be adapted to your practical needs.
  \end{itemize}
\end{frame}

\begin{frame}
  \frametitle{Practice}
  The practice will:
  \begin{itemize}
  \item be done within the terminal and a data mining platform, KNIME;
  \item consist of a few practical exercises;
  \item mainly consist of projects in groups of three students
    analyzing different and freely-chosen datasets.
  \end{itemize}
\end{frame}

\begin{frame}
  \frametitle{Assessment}
  The final mark will be based on:
  \begin{itemize}
  \item an exam on the POSIX text-processing commands (20 points);
  \item random questions on the content of the course (20 points);
  \item a non-trivial step of the project (10 points);
  \item the rest of the project (40 points);
  \item the clarity of a 10-page report (5 points);
  \item the clarity of a 20-minute presentation (5 points).
  \end{itemize}

  \vfill
  \pause

  \emph{Individual} adjustments based on:
  \begin{itemize}
  \item questions about the project (in particular, justifications);
  \item the help brought to other groups.
  \end{itemize}
\end{frame}

\begin{frame}
  \frametitle{Collaboration}
  \begin{quote}
    Collaboration is good.
  \end{quote}

  \vfill
  \pause

  Occasionally, every group will present problems it faces in their
  project and what they have already tried to solve them (five to ten
  minutes).  The group is \emph{not} assessed.

  \vfill
  \pause

  Students of other groups who help with ideas, remarks, and
  suggestions will gain bonus points.
\end{frame}

\begin{frame}
  \frametitle{A little bit of psychology}
  You (and I) have the natural tendency to prefer easy activities with
  short-term recompenses (watching blockbusters vs. watching
  documentaries, writing tweets vs. writing an article, eating candies
  vs. eating healthy, playing video games vs. doing homework, etc.).

  \vfill
  \pause

  The solution is not in time management but in ``metacognition''. To
  succeed in a project (in life?), the most important may not be the
  intelligence but the resistance to immediate desires.

  \vfill
  \pause

  By imposing a regular work, this ability is trained and, for sure,
  the resulting work will be better.
\end{frame}

% \begin{frame}
%   \frametitle{Defense dates}
%   \begin{description}
%   \item[26/11 at 17:00] Football Manager;
%   \item[26/11 at 17:50] SPOJ;
%   \item[01/12 at 17:00] Voos;
%   \item[01/12 at 17:50] Bittorrent;
%   \item[03/12 at 17:00] Web behavior;
%   \item[03/12 at 17:50] CEMIG.
%   \end{description}

%   \vfill

%   \emph{All} final reports must be sent by November, 24$^{\text{th}}$
%   at 23:59 (5-point penalty for every additional day).

%   \vfill

%   Retake exam: 10/12 at 17:00.
% \end{frame}

\begin{frame}
  \frametitle{Outline}
  \tableofcontents
\end{frame}

\section{Many perspectives on data mining}

\begin{frame}
  \frametitle{Epistemological perspective}
  \begin{itemize}[<+->]
  \item \emph{Knowledge} passes through patterns;
  \item It is \emph{acquired} from data;
  \item It is \emph{assessed} by quality measures.
  \end{itemize}

  \vfill

  \uncover<4->
  {
    Data mining is an \emph{empiricism}.
  }

  \vfill

  \uncover<5>
  {
    Notice however that it is necessarily biased (choice of a type of
    model/pattern, of a quality measure to optimize, etc.).  A bias
    toward simplicity is usually preferred (Occam's razor).
  }
\end{frame}

\begin{frame}
  \frametitle{Computational statistics perspective}
  Patterns summarize the data.  A balance is sought between the
  simplicity of the model and how well it explains the observed data.

  \vfill

  \emph{Computational} statistics aims to design efficient algorithms
  that implement statistical methods.

  \vfill
  \pause


  \vfill

  \begin{block}{Typical computational statistics task}
    \emph{Representative-based} clustering: summarizing data as a
    mixture of distributions maximizing a likelihood function.
  \end{block}
\end{frame}

\begin{frame}
  \frametitle{Machine learning perspective}
  Patterns are an abstraction of the data. They are information an
  algorithm can learn from.

  \vfill

  Machine learning focuses on the different ways to learn from
  data. It is the artificial intelligence side of data mining. It has
  strong ties with computational statistics and mathematical
  optimization.

  \vfill
  \pause

  \begin{block}{Typical machine-learning task}
    \emph{Supervised} classification: learning, from the descriptions
    of classified objects, what characterizes every class.
  \end{block}
\end{frame}

\begin{frame}
  \frametitle{Data mining perspective}
  From data to databases to data warehouses to inductive databases.

  \vfill

  Patterns are the results of complex queries an analyst makes.

  \vfill
  \pause

  \begin{block}{Typical data-mining task}
    \emph{Local} pattern discovery: enumerating subsets of a dataset
    that stand out of the rest of it.
  \end{block}
\end{frame}

\begin{frame}
  \frametitle{Inductive databases}
  \only<1>
  {
    Querying data:
    \begin{displaymath}
      \{d \in D \mid q(d, D)\}
    \end{displaymath}
    where:
    \begin{itemize}
    \item $D$ is a dataset (tuples),
    \item $q$ is a query.
    \end{itemize}
  }\only<2>
  {
    Querying patterns:
    \begin{displaymath}
      \{p \in P \mid \mathcal{Q}(p, D)\}
    \end{displaymath}
    where:
    \begin{itemize}
    \item $P$ is the pattern space,
    \item $D$ is the dataset,
    \item $\mathcal{Q}$ is an inductive query.
    \end{itemize}
  }
\end{frame}

\begin{frame}
  \frametitle{Big data perspective}
  Thanks to greater and greater computing capabilities, \emph{large}
  datasets can be stored. \emph{Big data} analytics is about
  understanding these many data (what usually requires parallel
  computing).

  \vfill
  \pause

  \begin{exampleblock}{Microarray analysis}
    Genomes are now easily sequenced. The current challenge is to
    understand the expression mechanism (from genomics to proteomics
    to phenotypes). DNA chips give the expression levels of tens of
    thousands of genes in different samples.
  \end{exampleblock}

  \vfill
  \pause

  Data mining methods are designed with time and space complexities in
  mind.
\end{frame}

\begin{frame}
  \frametitle{Data science perspective}
  Data science takes an application perspective. It encompasses
  everything that can be done on data from a specific field, including
  computational statistics, machine learning, and data mining. It
  emphasizes the necessity to understand the application domain.
\end{frame}

\begin{frame}
  \frametitle{Business intelligence perspective}
  Patterns allow to better understand the ``activity'' of a company,
  so that its managers can take better \emph{decisions}.

  \vfill
  \pause

  Besides data mining, the business intelligence emphasizes
  heterogeneous data, reporting, visualization, etc.
\end{frame}

\begin{frame}
  \frametitle{Big brother perspective}
  Knowing everything about everyone is a new business\ldots

  \vfill
  \pause

  \ldots and a political threat.  Collecting large amounts of personal
  data in centralized repositories is unethical: these data
  \emph{will} be eventually misused.  Data anonymization techniques
  can be employed.

  \vfill
  \pause

  Be responsible!
\end{frame}

\begin{frame}
  \frametitle{Buzz words}
  \emph{Data mining} is also known as \emph{information discovery},
  \emph{knowledge discovery}, \emph{Knowledge Discovery in Databases}
  (KDD), \emph{data analytics}, \emph{cognitive computing}, etc.
\end{frame}

\begin{frame}
  \frametitle{Absence of a unified theory}
  Data mining is a collection of tasks. Each of them can be solved by
  various techniques. These techniques are applicable on particular
  types of data only.

  \vfill
  \pause

  There is no unified theory of data mining.
\end{frame}

\section{The pattern discovery process}

\begin{frame}
  \frametitle{The naive view of pattern discovery}
    \begin{center}
      \begin{tabular}{ccc}
        & \includegraphics[width=.075\textwidth]{img/gear} & \\
        \includegraphics[width=.15\textwidth]{img/database}
        & \includegraphics[width=.15\textwidth]{img/pointer}
        & \includegraphics[width=.15\textwidth]{img/database2}\\
        Raw data & Extraction & Patterns
      \end{tabular}
  \end{center}

  \vfill

  \begin{block}{\tiny \copyright 2005 Tim Morgan (from
      flickr$^{\text{\textregistered}}$)}
    \tiny \includegraphics[scale=.15]{img/licenses/cc-by}\hskip1pt
    These icons are licensed under the Creative Commons Attribution
    2.0 License.
  \end{block}
\end{frame}

\begin{frame}
  \frametitle{The pattern discovery process}
    \begin{center}
      \begin{tabular}{ccccc}
        & \includegraphics[width=.075\textwidth]{img/gear} &
        & \includegraphics[width=.075\textwidth]{img/gear} & \\
        \includegraphics[width=.15\textwidth]{img/database}
        & \includegraphics[width=.15\textwidth]{img/pointer}
        & \includegraphics[width=.15\textwidth]{img/paper}
        & \includegraphics[width=.15\textwidth]{img/pointer}
        & \includegraphics[width=.15\textwidth]{img/database2}\\
        \only<1,3>{\includegraphics[width=.075\textwidth]{img/pointer_-135}}\only<2>{\includegraphics[width=.075\textwidth]{img/pointer_-135_alert}}
        & \only<-2>{\includegraphics[width=.075\textwidth]{img/fellowship}}\only<3>{\includegraphics[width=.075\textwidth]{img/fellowship_alert}}
        & \only<1,3>{\includegraphics[width=.075\textwidth]{img/pointer_180}}\only<2>{\includegraphics[width=.075\textwidth]{img/pointer_180_alert}}
        & \only<-2>{\includegraphics[width=.075\textwidth]{img/fellowship}}\only<3>{\includegraphics[width=.075\textwidth]{img/fellowship_alert}}
        & \only<1,3>{\includegraphics[width=.075\textwidth]{img/pointer_135}}\only<2>{\includegraphics[width=.075\textwidth]{img/pointer_135_alert}}\\
        Raw data & Pre-process & Data & Extraction & Patterns
      \end{tabular}
  \end{center}

  \vfill

  \begin{block}{\tiny \copyright 2005 Tim Morgan (from
      flickr$^{\text{\textregistered}}$)}
    \tiny \includegraphics[scale=.15]{img/licenses/cc-by}\hskip1pt
    These icons are licensed under the Creative Commons Attribution
    2.0 License.
  \end{block}
\end{frame}

\section*{}

\begin{frame}
  \frametitle{License}
  \begin{block}{\copyright 2011--2025 Loïc Cerf}
    \includegraphics[height=1.5ex]{img/licenses/cc-by-sa}\hskip1pt
    These slides are licensed under the Creative Commons
    Attribution-ShareAlike 4.0 International License.
  \end{block}
\end{frame}

\end{document}
