Latex第一次上手尝试

选了专业英语课，了解了Latex的基本操作，记录一下自己的第一个作品。

附上实现的代码

\documentclass{article}
\usepackage{inputenc}
\usepackage{algorithm}
\usepackage{algorithmic}  
\usepackage{graphicx} 
\usepackage{url}
\usepackage{pgfplots}
\usepackage{pgfplotstable}
\pgfplotsset{width=7cm,compat=1.13}

\title{Design and Implementation of Algorithm for Frequent Item Mining in Hadoop}
\author{Jipeng Liu }
\date{Last updated: \today}

\begin{document}

\maketitle

\begin{abstract}
    The main work of this paper is to carry out the parallel design and implementation of the Apriori algorithm on the Hadoop platform. By using the cluster to carry out distributed computing, it can effectively solve the problem that the processing speed of the serial algorithm in the face of large-scale data is very slow or the data can not be handled, so that the frequent itemset mining can be accomplished efficiently. Through the experiment, it not only validates the efficient processing of the parallel Apriori algorithm for large-scale data, but also proves that the algorithm has good scalability, and can increase the efficiency of execution by increasing the number of nodes in the cluster.
\end{abstract}

\section{Introduction}
With the advent of the era of big data, a large amount of information will be generated every day. The work of data mining is to excavate valuable content from these massive information. This technology has been widely used in the fields of finance, medical treatment, Internet and so on. Frequent itemsets mining is an important branch of data mining and plays an important role in business, bioinformatics, weather forecasting and other fields.  Some people \cite{refb} show the importance of data mining.And others \cite{ref1} give an algorithm.  

\section{Related Work}
Apriori algorithm is an effective algorithm for frequent itemsets mining. Since it was put forward, it has been continuously optimized and improved by scientific researchers.

\section{Preliminary Knowledge }
We will introduce some knowledge about Frequent Itemset and Apriori Algorithm in this section. 

\subsection{The Conception of Frequent Itemset}
Frequent itemsets mining is an important branch of data mining and plays an important role in business, bioinformatics, weather forecasting and other fields. Two main equations are $y=ax+bx+c$ and equation \ref{eq}.

\begin{equation}
\frac{\pi}{4} = \sum_{k=1}^\infty  \frac{(-1)^{k+1}}{2k-1} 
\label{eq}
\end{equation}
                                                 
\subsection{Introduction to Apriori Algorithm}
Our Aprior algorithm is shown in Algorithm 1.

\begin{algorithm}[!h]
	\caption{PARTITION$(A,p,r)$}
	\begin{algorithmic}[1]
		\STATE $i=p$
		\FOR{$j=p$ to $r$}
		\IF{$A[j]<=0$}
		\STATE $swap(A[i],A[j])$
		\STATE $i=i+1$
		\ENDIF
		\ENDFOR
	\end{algorithmic}
\end{algorithm}

\section{Performance Results }
The detailed information of these devices is listed in Table \ref{example}. Optane is a PCIe-attached device using 3D XPoint technology, while NAND is a traditional SATA attached device based on NAND flash, and RAMDISK is an emulated device using DRAM. 

\begin{table}[htp] 
\center
\begin{tabular}{|c|r|l|p{1in}|} 
\hline DEVICE &NAND& OPTANE & RAMDISK \\
\hline Device Model &Intel S3510 & Intel Optane 900P & Micron DDR4 \\ 
\hline Interface & SATA 3.0 &PCIe 3.0 X 4&DIMM\\
\hline Memory Medium &NAND flash&3D XPoint &DRAM\\
\hline
\end{tabular} 
\caption{Hardware Devices}
\label{example} 
\end{table}

\section{Conclusion }
We will show our experiment result in Figure 1 and our cache model will be in Figure \ref{pic1}.

\begin{figure}[htp]
\centering
\begin{tikzpicture}
\begin{axis}[legend pos=outer north east] 
\addplot 
table                               
{           		                
 X Y
 1 1
 2 4
 3 9
 4 16 
 5 25
 6 36
};
\addplot
table[y={create col/linear regression={y=Y}}] 
{   				
 X Y
 1 1
 2 4
 3 9
 4 16 
 5 25
 6 36
};
\addlegendentry{$y(x)$}          
\addlegendentry{                 
$\pgfmathprintnumber{\pgfplotstableregressiona} \cdot x
\pgfmathprintnumber[print sign]{\pgfplotstableregressionb}$}
\end{axis}
\end{tikzpicture}
\label{pic}
\caption{Result}
\end{figure}

\begin{figure}[htp] 
\centering 
\includegraphics[width=3in]{2.PNG} 
\caption{A Cache Model. Image source: \protect\url{http://pic31.photophoto.cn/20140405/0014027020007350_b.jpg}} 
\label{pic1} 
\end{figure}


\clearpage
\bibliographystyle{siam} 
\bibliography{ref,ref2}

\end{document}