The Roots of Web Scraping and the Wisdom behind It

You may be wondering how data mining came into existence. This effective and innovative trend in business and research is indeed something commendable and the genius behind it is worth great reward. To have a clear view of the origin of web scraping, the following important factors that contribute to the creation of this phenomenon called data collection or web scraping are considered.

Foundations

Unlike any other innovation, no specific date can be clearly pointed out as the birthdate of data mining. It has come into existence as a result of several problem solving processes in major data gathering and handling situations. It appears that cyber technology has opened a Pandora box of “anything can happen” experiences. Moreover, the shift from physical to virtual data collection has resulted in a bulk of database that needed to be organized, analyzed and utilized.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/roots-web-scraping-wisdom-behind/

Data Mining and Frequent Datasets

I've been doing some work for my exams in a few days and I'm going through some past papers but unfortunately there are no corresponding answers. I've answered the question and I was wondering if someone could tell me if I am correct.

My question is

    (c) A transactional dataset, T, is given below:
    t1: Milk, Chicken, Beer
    t2: Chicken, Cheese
    t3: Cheese, Boots
    t4: Cheese, Chicken, Beer,
    t5: Chicken, Beer, Clothes, Cheese, Milk
    t6: Clothes, Beer, Milk
    t7: Beer, Milk, Clothes

    Assume that minimum support is 0.5 (minsup = 0.5).

    (i) Find all frequent itemsets.

Here is how I worked it out:

    Item : Amount
    Milk : 4
    Chicken : 4
    Beer : 5
    Cheese : 4
    Boots : 1
    Clothes : 3

Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:

    {items} : Amount
    {Milk, Chicken} : 2
    {Milk, Beer} : 4
    {Milk, Cheese} : 1
    {Chicken, Beer} : 3
    {Chicken, Cheese} : 3
    {Beer, Cheese} : 2

Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?

data mining

Nanor

3 Answers

There are two ways to solve the problem:

    using Apriori algorithm
    Using FP counting

Assuming that you are using Apriori, the answer you got is correct.

The algorithm is simple:

First you count frequent 1-item sets and exclude the item-sets below minimum support.

Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.

The algorithm can go on until no item-sets are greater than threshold.

In the problem given to you, you only get 1 set of 2 items greater than threshold so you can't move further.

There is a solved example of further steps on Wikipedia here.

You can refer "Data Mining Concepts and Techniques" by Han and Kamber for more examples.

141

There is more than two algorithms to solve this problem. I will just mention a few of them: Apriori, FPGrowth, Eclat, HMine, DCI, Relim, AIM, etc. –  Phil Mar 5 '13 at 7:18

OK to start, you must first understand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Now, the amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database.

Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.

Now you must understand that association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule.

Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in real- life applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low.

This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.

Given a set of transactions T (the database), the problem of mining association rules is to discover all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf).

I hope that once you understand the very basics of data mining that the answer to this question shall become apparent.

1

The Apriori algorithm is based on the idea that for a pair o items to be frequent, each individual item should also be frequent. If the hamburguer-ketchup pair is frequent, the hamburger itself must also appear frequently in the baskets. The same can be said about the ketchup.

So for the algorithm, it is established a "threshold X" to define what is or it is not frequent. If an item appears more than X times, it is considered frequent.

The first step of the algorithm is to pass for each item in each basket, and calculate their frequency (count how many time it appears). This can be done with a hash of size N, where the position y of the hash, refers to the frequency of Y.

If item y has a frequency greater than X, it is said to be frequent.

In the second step of the algorithm, we iterate through the items again, computing the frequency of pairs in the baskets. The catch is that we compute only for items that are individually frequent. So if item y and item z are frequent on itselves, we then compute the frequency of the pair. This condition greatly reduces the pairs to compute, and the amount of memory taken.

Once this is calculated, the frequencies greater than the threshold are said frequent itemset.

Source: http://stackoverflow.com/questions/14164853/data-mining-and-frequent-datasets?rq=1

Online Data Entry & Web Scraping Services

To operate any type of organization smoothly, it is essential to have precise data that is accurate and reliable. When your business expands, data entry on an ongoing basis is a tedious job. It’s a very time consuming task that can often distract employees focusing on core business areas.

Webpop offers all forms of online data entry services that are quick and accurate. We provide data entry services across all verticals that can be completely customized to your business requirements.

Database Population Services

Database population involves content collection from various database sources. This requires a lot of attention to detail, dedication and awareness and can prove a formidable task, especially for websites that largeley depend on it.

Webpop offer a quick and efficient database population service that helps relieve the stress from an extremely laborius task and leaves you more time to focus on more important aspects of your business. By investing just a fraction of the cost, you can outsource your database population tasks to us.

Web Scraping Services

Webpop have been assisting clients in searching, extracting and collecting data from the web for the past 5 years using the latest techniques in web scraping techology. We can scrape all types of information from a variety of sources such as websites, blogs, online directories, e-commerce websites and podcasts to name a few. We use a varied selection of automated and manual web scraping technologies to extract, gather and collect all of the required data you require from any chosen website(s) on the World Wide Web.

We can simplify the whole process from collection to population, converting your scraped data in to structured formats that are applicable to your website. This can be offered as a one time service or an ongoing basis that will assist you in constantly keeping your website’s content fresh and up to date. We can crawl competitors websites, gather sales leads, product details, pricing methodologies and also creat custom campaigns to suit your project’s requirements.

Over the years Webpop has grown from strength-to-strength by providing all types of data entry, database population and web scraping services. All of our data entry services are performed with care, due dilligence and attention to detail. We enjoy a challenge and pride ourselves on delivering results whilst working on precarious projects that require precision and total commitment.

Source:http://www.webpopdesign.com/services/data-entry/

Screen-scraping with WWW::Mechanize

Screen-scraping is the process of emulating an interaction with a Web site - not just downloading pages, but filling out forms, navigating around the site, and dealing with the HTML received as a result. As well as for traditional lookups of information - like the example we'll be exploring in this article - we can use screen-scraping to enhance a Web service into doing something the designers hadn't given us the power to do in the first place. Here's an example:

I do my banking online, but get quickly bored with having to go to my bank's site, log in, navigate around to my accounts and check the balance on each of them. One quick Perl module (Finance::Bank::HSBC) later, and now I can loop through each of my accounts and print their balances, all from a shell prompt. Some more code, and I can do something the bank's site doesn't ordinarily let me - I can treat my accounts as a whole instead of individual accounts, and find out how much money I have, could possibly spend, and owe, all in total.

Another step forward would be to schedule a crontab every day to use the HSBC option to download a copy of my transactions in Quicken's QIF format, and use Simon Cozens' Finance::QIF module to interpret the file and run those transactions against a budget, letting me know whether I'm spending too much lately. This takes a simple Web-based system from being merely useful to being automated and bespoke; if you can think of how to write the code, then you can do it. (It's probably wise for me to add the caveat, though, that you should be extremely careful working with banking information programatically, and even more careful if you're storing your login details in a Perl script somewhere.)

Back to screen-scrapers, and introducing WWW::Mechanize, written by Andy Lester and based on Skud's WWW::Automate. Mechanize allows you to go to a URL and explore the site, following links by name, taking cookies, filling in forms and clicking "submit" buttons. We're also going to use HTML::TokeParser to process the HTML we're given back, which is a process I've written about previously.

The site I've chosen to demonstrate on is the BBC's Radio Times site, which allows users to create a "Diary" for their favorite TV programs, and will tell you whenever any of the programs is showing on any channel. Being a London Perl M[ou]nger, I have an obsession with Buffy the Vampire Slayer. If I tell this to the BBC's site, then it'll tell me when the next episode is, and what the episode name is - so I can check whether it's one I've seen before. I'd have to remember to log into their site every few days to check whether there was a new episode coming along, though. Perl to the rescue! Our script will check to see when the next episode is and let us know, along with the name of the episode being shown.

Here's the code:

  #!/usr/bin/perl -w
  use strict;
  use WWW::Mechanize;
  use HTML::TokeParser;

If you're going to run the script yourself, then you should register with the Radio Times site and create a diary, before giving the e-mail address you used to do so below.

  my $email = ";
  die "Must provide an e-mail address" unless $email ne ";

We create a WWW::Mechanize object, and tell it the address of the site we'll be working from. The Radio Times' front page has an image link with an ALT text of "My Diary", so we can use that to get to the right section of the site:

  my $agent = WWW::Mechanize->new();
  $agent->get("http://www.radiotimes.beeb.com/");
  $agent->follow("My Diary");

The returned page contains two forms - one to allow you to choose from a list box of program types, and then a login form for the diary function. We tell WWW::Mechanize to use the second form for input. (Something to remember here is that WWW::Mechanize's list of forms, unlike an array in Perl, is indexed starting at 1 rather than 0. Our index is, therefore,'2.')

  $agent->form(2);

Now we can fill in our e-mail address for the '<INPUT name="email" type="text">' field, and click the submit button. Nothing too complicated.

  $agent->field("email", $email);
  $agent->click();

WWW::Mechanize moves us to our diary page. This is the page we need to process to find the date details from. Upon looking at the HTML source for this page, we can see that the HTML we need to work through is something like:

  <input>
  <tr><td></td></tr>
  <tr><td></td><td></td><td class="bluetext">Date of episode</td></tr>
  <td></td><td></td>
  <td class="bluetext"><b>Time of episode</b></td></tr>
  <a href="page_with_episode_info"></a>

This can be modeled with HTML::TokeParser as below. The important methods to note are get_tag - which will move the stream on to the next opening for the tag given - and get_trimmed_text, which returns the text between the current and given tags. For example, for the HTML code "<b>Bold text here</b>", my $tag = get_trimmed_text("/b") would return "Bold text here" to $tag.

Also note that we're initializing HTML::TokeParser on '\$agent->{content}' - this is an internal variable for WWW::Mechanize, exposing the HTML content of the current page.

  my $stream = HTML::TokeParser->new(\$agent->{content});
  my $date;
    # <input>
  $stream->get_tag("input");
  # <tr><td></td></tr><tr>
  $stream->get_tag("tr"); $stream->get_tag("tr");
  # <td></td><td></td>
  $stream->get_tag("td"); $stream->get_tag("td");
  # <td class="bluetext">Date of episode</td></tr>
  my $tag = $stream->get_tag("td");
  if ($tag->[1]{class} and $tag->[1]{class} eq "bluetext") {
      $date = $stream->get_trimmed_text("/td");
      # The date contains '&nbsp;', which we'll translate to a space.
      $date =~ s/\xa0/ /g;
  }
   # <td></td><td></td>
  $stream->get_tag("td");
  # <td class="bluetext"><b>Time of episode</b> 
  $tag = $stream->get_tag("td");
  if ($tag->[1]{class} eq "bluetext") {
      $stream->get_tag("b");
      # This concatenates the time of the showing to the date.
      $date .= ", from " . $stream->get_trimmed_text("/b");
  }
  # </td></tr><a href="page_with_episode_info"></a>
  $tag = $stream->get_tag("a");
  # Match the URL to find the page giving episode information.
  $tag->[1]{href} =~ m!src=(http://.*?)'!;

We have a scalar, $date, containing a string that looks something like "Thursday 23 January, from 6:45pm to 7:30pm.", and we have an URL, in $1, that will tell us more about that episode. We tell WWW::Mechanize to go to the URL:

  $agent->get($1);

The navigation we want to perform on this page is far less complex than on the last page, so we can avoid using a TokeParser for it - a regular expression should suffice. The HTML we want to parse looks something like this:

  <br><b>Episode</b><br>  The Episode Title<br>

We use a regex delimited with '!' in order to avoid having to escape the slashes present in the HTML, and store any number of alphanumeric characters after some whitespace, all between <br> tags after the Episode header:

  $agent->{content} =~ m!<br><b>Episode</b><br>\s+?(\w+?)<br>!;

$1 now contains our episode, and all that's left to do is print out what we've found:

  my $episode = $1;
  print "The next Buffy episode ($episode) is on $date.\n";

And we're all set. We can run our script from the shell:

  $ perl radiotimes.pl

  The next Buffy episode (Gone) is Thursday Jan. 23, from 6:45 to 7:30 p.m.
I hope this gives a light-hearted introduction to the usefulness of the modules involved. As a note for your own experiments, WWW::Mechanize supports cookies - in that the requestor is a normal LWP::UserAgent object - but they aren't enabled by default. If you need to support cookies, then your script should call "use HTTP::Cookies; $agent->cookie_jar(HTTP::Cookies->new);" on your agent object in order to enable session-volatile cookies for your own code.
Happy screen-scraping, and may you never miss a Buffy episode again.

Source: http://www.perl.com/pub/2003/01/22/mechanize.html

How to scrape Amazon with WebDriver in Java

Here is a real-world example of using Selenium WebDriver for scraping.
This short program is written in Java and scrapes book title and author from the Amazon webstore.
This code scrapes only one page, but you can easily make it scraping all the pages by adding a couple of lines.

You can download the souce here.

import java.io.*;
import java.util.*;
import java.util.regex.*;

import org.openqa.selenium.*;
import org.openqa.selenium.firefox.FirefoxDriver;

public class FetchAllBooks {

    public static void main(String[] args) throws IOException {

        WebDriver driver = new FirefoxDriver();
      

driver.navigate().to("http://www.amazon.com/tag/center%20right?ref_=tag_dpp_cust_itdp_s_t&sto

re=1");

        List<WebElement> allAuthors =  driver.findElements(By.className("tgProductAuthor"));
        List<WebElement> allTitles =  driver.findElements(By.className("tgProductTitleText"));
        int i=0;
        String fileText = "";

        for (WebElement author : allAuthors){
            String authorName = author.getText();
            String Url = (String)((JavascriptExecutor)driver).executeScript("return

arguments[0].innerHTML;", allTitles.get(i++));
            final Pattern pattern = Pattern.compile("title=(.+?)>");
            final Matcher matcher = pattern.matcher(Url);
            matcher.find();
            String title = matcher.group(1);
            fileText = fileText+authorName+","+title+"\n";
        }

        Writer writer = new BufferedWriter(new OutputStreamWriter(new

FileOutputStream("books.csv"), "utf-8"));
        writer.write(fileText);
        writer.close();

        driver.close();
    }
}

Source: http://scraping.pro/scraping-amazon-webdriver-java/

Why Web Scraping is Indispensable

The 21st century has opened the gates to hidden treasures and unlimited access to information globally without the constraints of time and space, through Internet technology. Along with this development comes the necessity for each business or company to get as much information as possible in order in order to thrive in the ever increasing demand for new innovations, comparisons, and trends.

Web scraping has consequently become an indispensable option to achieve all the needed data as quickly and efficiently as possible. In this view, data mining then appears to be the best and the only way to answer the present demand for updates, data, coping, foreknowledge, analysis, and evaluation. Indeed, information has inevitably become a valuable commodity and the most sought after product among online and offline entrepreneurs.

Need for Data

The increasing need for new data makes it possible for the experts to become increasingly creative in accessing information worldwide. The more knowledge one has, the better are his or her chances of growing and surviving. There seems to be no other time in the human existence where data has become so much a major source of revenue as the contemporary times.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-indispensable/