Using Charts For Effective Data Mining

Using Charts For Effective Data Mining

The modern world is one where data is gathered voraciously. Modern computers with all their advanced hardware and software are bringing all of this data to our fingertips. In fact one survey says that the amount of data gathered is doubled every year. That is quite some data to understand and analyze. And this means a lot of time, effort and money. That is where advancements in the field of Data Mining have proven to be so useful.

Data mining is basically a process of identifying underlying patters and relationships among sets of data that are not apparent at first glance. It is a method by which large and unorganized amounts of data are analyzed to find underlying connections which might give the analyzer useful insight into the data being analyzed.

It's uses are varied. In marketing it can be used to reach a product to a particular customer. For example, suppose a supermarket while mining through their records notices customers preferring to buy a particular brand of a particular product. The supermarket can then promote that product even further by giving discounts, promotional offers etc. related to that product. A medical researcher analyzing D.N.A strands can and will have to use data mining to find relationships existing among the strands. Apart from bio-informatics, data mining has found applications in several other fields like genetics, pure medicine, engineering, even education.

The Internet is also a domain where mining is used extensively. The world wide web is a minefield of information. This information needs to be sorted, grouped and analyzed. Data Mining is used extensively here. For example one of the most important aspects of the net is search. Everyday several million people search for information over the world wide web. If each search query is to be stored then extensively large amounts of data will be generated. Mining can then be used to analyze all of this data and help return better and more direct search results which lead to better usability of the Internet.

Data mining requires advanced techniques to implement. Statistical models, mathematical algorithms or the more modern machine learning methods may be used to sift through tons and tons of data in order to make sense of it all.

Foremost among these is the method of charting. Here data is plotted in the form of charts and graphs. Data visualization, as it is often referred to is a tried and tested technique of data mining. If visually depicted, data easily reveals relationships that would otherwise be hidden. Bar charts, pie charts, line charts, scatter plots, bubble charts etc. provide simple, easy techniques for data mining.

Thus a clear simple truth emerges. In today's world of heavy load data, mining it is necessary. And charts and graphs are one of the surest methods of doing this. And if current trends are anything to go by the importance of data mining cannot be undermined in any way in the near future.

Source : http://ezinearticles.com/?Using-Charts-For-Effective-Data-Mining&id=2644996

Why Outsourcing Data Mining Services?

Why Outsourcing Data Mining Services?

Are huge volumes of raw data waiting to be converted into information that you can use? Your organization's hunt for valuable information ends with valuable data mining, which can help to bring more accuracy and clarity in decision making process.

Nowadays world is information hungry and with Internet offering flexible communication, there is remarkable flow of data. It is significant to make the data available in a readily workable format where it can be of great help to your business. Then filtered data is of considerable use to the organization and efficient this services to increase profits, smooth work flow and ameliorating overall risks.

Data mining is a process that engages sorting through vast amounts of data and seeking out the pertinent information. Most of the instance data mining is conducted by professional, business organizations and financial analysts, although there are many growing fields that are finding the benefits of using in their business.

Data mining is helpful in every decision to make it quick and feasible. The information obtained by it is used for several applications for decision-making relating to direct marketing, e-commerce, customer relationship management, healthcare, scientific tests, telecommunications, financial services and utilities.

Data mining services include:

    Congregation data from websites into excel database
    Searching & collecting contact information from websites
    Using software to extract data from websites
    Extracting and summarizing stories from news sources
    Gathering information about competitors business

In this globalization era, handling your important data is becoming a headache for many business verticals. Then outsourcing is profitable option for your business. Since all projects are customized to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure can be realized.

Advantages of Outsourcing Data Mining Services:

    Skilled and qualified technical staff who are proficient in English
    Improved technology scalability
    Advanced infrastructure resources
    Quick turnaround time
    Cost-effective prices
    Secure Network systems to ensure data safety
    Increased market coverage

Outsourcing will help you to focus on your core business operations and thus improve overall productivity. So data mining outsourcing is become wise choice for business. Outsourcing of this services helps businesses to manage their data effectively, which in turn enable them to achieve higher profits.

Source : http://ezinearticles.com/?Why-Outsourcing-Data-Mining-Services?&id=3066061

Know What the Truth Behind Data Mining Outsourcing Service

Know What the Truth Behind Data Mining Outsourcing Service

We came to that, what we call the information age where industries are like useful data needed for decision-making, the creation of products - among other essential uses for business. Information mining and converting them to useful information is a part of this trend that allows companies to reach their optimum potential. However, many companies that do not meet even one deal with data mining question because they are simply overwhelmed with other important tasks. This is where data mining outsourcing comes in.

There have been many definitions to introduced, but it can be simply explained as a process that involves sorting through large amounts of raw data to extract valuable information needed by industries and enterprises in various fields. In most cases this is done by professionals, professional organizations and financial analysts. He has seen considerable growth in the number of sectors or groups that enter my self.
There are a number of reasons why there is a rapid growth in data mining outsourcing service subscriptions. Some of them are presented below:

A wide range of services

Many companies are turning to information mining outsourcing, because they cover a wide range of services. These services include, but are not limited to data from web applications congregation database, collect contact information from different sites, extract data from websites using the software, the sort of stories from sources news, information and accumulate commercial competitors.

Many companies fall

Many industries benefit because it is fast and realistic. The information extracted by data mining service providers of outsourcing used in crucial decisions in the field of direct marketing, e-commerce, customer relationship management, health, scientific tests and other experimental work, telecommunications, financial services, and a whole lot more.

A lot of advantages

Subscribe data mining outsourcing services it's offers many benefits, as providers assures customers to render services to world standards. They strive to work with improved technologies, scalability, sophisticated infrastructure, resources, timeliness, cost, the system safer for the security of information and increased market coverage.

Outsourcing allows companies to focus their core business and can improve overall productivity. Not surprisingly, information mining outsourcing has been a first choice of many companies - to propel the business to higher profits.

Source:http://ezinearticles.com/?Know-What-the-Truth-Behind-Data-Mining-Outsourcing-Service&id=5303589

Data Extraction - A Guideline to Use Scrapping Tools Effectively

Data Extraction - A Guideline to Use Scrapping Tools Effectively

So many people around the world do not have much knowledge about these scrapping tools. In their views, mining means extracting resources from the earth. In these internet technology days, the new mined resource is data. There are so many data mining software tools are available in the internet to extract specific data from the web. Every company in the world has been dealing with tons of data, managing and converting this data into a useful form is a real hectic work for them. If this right information is not available at the right time a company will lose valuable time to making strategic decisions on this accurate information.

This type of situation will break opportunities in the present competitive market. However, in these situations, the data extraction and data mining tools will help you to take the strategic decisions in right time to reach your goals in this competitive business. There are so many advantages with these tools that you can store customer information in a sequential manner, you can know the operations of your competitors, and also you can figure out your company performance. And it is a critical job to every company to have this information at fingertips when they need this information.

To survive in this competitive business world, this data extraction and data mining are critical in operations of the company. There is a powerful tool called Website scraper used in online digital mining. With this toll, you can filter the data in internet and retrieves the information for specific needs. This scrapping tool is used in various fields and types are numerous. Research, surveillance, and the harvesting of direct marketing leads is just a few ways the website scraper assists professionals in the workplace.

Screen scrapping tool is another tool which useful to extract the data from the web. This is much helpful when you work on the internet to mine data to your local hard disks. It provides a graphical interface allowing you to designate Universal Resource Locator, data elements to be extracted, and scripting logic to traverse pages and work with mined data. You can use this tool as periodical intervals. By using this tool, you can download the database in internet to you spread sheets. The important one in scrapping tools is Data mining software, it will extract the large amount of information from the web, and it will compare that date into a useful format. This tool is used in various sectors of business, especially, for those who are creating leads, budget establishing seeing the competitors charges and analysis the trends in online. With this tool, the information is gathered and immediately uses for your business needs.

Another best scrapping tool is e mailing scrapping tool, this tool crawls the public email addresses from various web sites. You can easily from a large mailing list with this tool. You can use these mailing lists to promote your product through online and proposals sending an offer for related business and many more to do. With this toll, you can find the targeted customers towards your product or potential business parents. This will allows you to expand your business in the online market.

There are so many well established and esteemed organizations are providing these features free of cost as the trial offer to customers. If you want permanent services, you need to pay nominal fees. You can download these services from their valuable web sites also.

Source:http://ezinearticles.com/?Data-Extraction---A-Guideline-to-Use-Scrapping-Tools-Effectively&id=3600918

Collecting Data With Web Scrapers

Collecting Data With Web Scrapers

There is a large amount of data available only through websites. However, as many people have found out, trying to copy data into a usable database or spreadsheet directly out of a website can be a tiring process. Data entry from internet sources can quickly become cost prohibitive as the required hours add up. Clearly, an automated method for collating information from HTML-based sites can offer huge management cost savings.

Web scrapers are programs that are able to aggregate information from the internet. They are capable of navigating the web, assessing the contents of a site, and then pulling data points and placing them into a structured, working database or spreadsheet. Many companies and services will use programs to web scrape, such as comparing prices, performing online research, or tracking changes to online content.

Let's take a look at how web scrapers can aid data collection and management for a variety of purposes.

Improving On Manual Entry Methods

Using a computer's copy and paste function or simply typing text from a site is extremely inefficient and costly. Web scrapers are able to navigate through a series of websites, make decisions on what is important data, and then copy the info into a structured database, spreadsheet, or other program. Software packages include the ability to record macros by having a user perform a routine once and then have the computer remember and automate those actions. Every user can effectively act as their own programmer to expand the capabilities to process websites. These applications can also interface with databases in order to automatically manage information as it is pulled from a website.

Aggregating Information

There are a number of instances where material stored in websites can be manipulated and stored. For example, a clothing company that is looking to bring their line of apparel to retailers can go online for the contact information of retailers in their area and then present that information to sales personnel to generate leads. Many businesses can perform market research on prices and product availability by analyzing online catalogues.

Data Management

Managing figures and numbers is best done through spreadsheets and databases; however, information on a website formatted with HTML is not readily accessible for such purposes. While websites are excellent for displaying facts and figures, they fall short when they need to be analyzed, sorted, or otherwise manipulated. Ultimately, web scrapers are able to take the output that is intended for display to a person and change it to numbers that can be used by a computer. Furthermore, by automating this process with software applications and macros, entry costs are severely reduced.

This type of data management is also effective at merging different information sources. If a company were to purchase research or statistical information, it could be scraped in order to format the information into a database. This is also highly effective at taking a legacy system's contents and incorporating them into today's systems.

Overall, a web scraper is a cost effective user tool for data manipulation and management.

source: http://ezinearticles.com/?Collecting-Data-With-Web-Scrapers&id=4223877

Cleaning up Data Scraped from the Web

Cleaning up Data Scraped from the Web

This section deals with how you can clean up data – having extracted it from the web by scraping.
Useful clean up steps

One advantage of scraping data from the web is that you can actually have a better dataset than the original. Because you need to take steps to understand the dataset’s inconsistencies, you can eliminate or at least minimise them. From another perspective, spending time cleaning up messy data can fill the large gaps that your processor will experience when waiting for it to be downloaded from its host.

This section provides an example of several useful clean-up operations.

    Cleaning HTML
    Strip whitespace
    Converting numbers to number types:
    Converting Boolean values: ‘Yes’ -> True
    Converting dates to machine-readable formats: “24 June 2004” -> “2004-06-24”

Clean the HTML

HTML you find on the web can be atrocious. Here’s a quick function that can help. We make use of the `lxml`_ library. It’s very good at understanding broken HTML and will render a perfectly-formed page for your extractor functions.

You may be concerned that this is computationally wasteful. This is true, but it can reduce lots of the irritation of extracting specific information from messy HTML:

def clean_page(html, pretty_print=False):
    """
    >>> junk = "some random HTML<P> for you to try to parse</p>"
    >>> clean_page(junk)
    '<div><p>some random HTML</p><p> for you to try to parse</p></div>'
    >>> print clean_page(junk, pretty_print=True)
    <div>
    <p>some random HTML</p>
    <p> for you to try to parse</p>
    </div>
    """
    from lxml.html import fromstring
    from lxml.html import tostring
    return tostring(fromstring(html), pretty_print=pretty_print)

Converting yes/no to Boolean values

Computers are far better at interpreting Boolean values when they are consistently provided. Irrespective of the programming language, normalising these values will make any automatic comparisons much richer:

def to_bool(yes_no, none_to_false=True):
    """
    >>> to_bool('')
    False
    >>> to_bool(None):
    False
    >>> to_bool('y')
    True
    >>> to_bool('yip')
    True
    >>> to_bool('Yes')
    True
    >>> to_bool('nuh')
    False
    """
    yes_no = yes_no.strip().lower()
    if not yes_no.strip() and none_to_false:
        return False
    if yes_no.startswith('y'):
        return True
    elif yes_no.startswith('n'):
        return False

Converting numbers to the correct type

If you’re extracting numbers from HTML tables, they will each be represented as a string or Unicode, even though it would be more sensible to treat as integers or floating point numbers:

def to_int(number, european=False):
    """
    >>> to_int('32')
    32
    >>> to_int('3,998')
    3998
    >>> to_int('3.998', european=True)
    3998
    """
    if european:
        number = number.replace('.', '')
    else:
        number = number.replace(',', '')
    return int(number)

def to_float(number, european=False)
    """
    >>> to_float(u'42.1')
    42.1
    >>> to_float(u'32,1', european=True)
    32.1
    >>> to_float('3,132.87')
    3132.87
    >>> to_float('3.132,87')
    3132.87
    >>> to_float('(54.12)')
    -54.12

    Warning
    -------

    Incorrectly declaring `european` leads to troublesome results:

    >>> to_float('54.2', european=True)
    542
    """
    import string
    if european:
        table = string.maketrans(',.','.,')
        number = string.translate(number, table)
    number = number.replace(',', '')
    if number.startswith('(') and number.endswith(')'):
        number = '-' + number[1:-1]
return float(number)

If you are dealing with numbers from another region consistently, it may be appropriate to call upon the locale module. You will then have the advantage of code written in C, rather than Python:

>>> import locale
>>> locale.setlocale(locale.LC_ALL, '')
>>> locale.atoi('1,000,000')
1000000

Stripping whitespace

Removing whitespace from a string is built into many languages string. Removing left and right whitespace is highly recommended. Your database will be unable to sort data properly which have inconsistent treatment of whitespace:

>>> u'\n\tTitle'.strip()
u'Title'

Converting dates to a machine-readable format

Python is well blessed with a mature date parser, dateutil. We can take advantage of this to make light work an otherwise error-prone task.

dateutil can be reluctant to raise exceptions to dates that it doesn’t understand. Therefore, it can be wise to store the original along with the parsed ISO formatted string. This can be used for manual checking if required later.

Example code:

def date_to_iso(datestring):
    """
    Takes a string of a human-readable date and
    returns a machine-readable date string.

    >>> date_to_iso('20 July 2002')
    '2002-07-20 00:00:00'
    >>> date_to_iso('June 3 2009 at 4am')
    '2009-06-03 04:00:00'
    """
    from dateutil import parser
    from datetime import datetime
    default = datetime(year=1, month=1, day=1)
    return str(parser.parse(datestring, default=default))

Source: http://schoolofdata.org/handbook/recipes/cleaning-data-scraped-from-the-web/

How to scrape search results from search engines like Google, Bing and Yahoo

How to scrape search results from search engines like Google, Bing and Yahoo

Search giants like Google, Yahoo and Bing made their empire on scraping others content. However, they don’t want you to scrape them. How ironic, isn’t it?

Search engine performance is a very important metric all digital marketers want to measure and improve. I’m sure you will be using some great SEO tools to check how your keywords perform. All great SEO tool comes with a search keyword ranking feature. The tools will tell you how your keywords are performing in google, yahoo bing etc.

 How will you get data from search engines If you want to build a keyword ranking app?

 These search engines have API’s but the daily query limit is very low and not useful for the commercial purpose. The only solution is to scrape search results. Search engine giants obviously know this :). Once they know that you are scraping, they will  block your IP, Period!

 How do Search engines detect bots?

 Here are the common methods of detection of bots.

* IP address: Search engines can detect if there are too many requests coming from a single IP. If a high amount of traffic is detected, they will throw a captcha.

 * Search patterns: Search engines match traffic patterns to an existing set of patterns and if there is huge variation, they will classify this as a bot.

 If you don’t have access to sophisticated technology, it is impossible to scrape search engines like google, Bing or Yahoo.

 How to avoid detection

There are some things you can do to  avoid detection.

    Scrape slowly and don’t try to squeeze everything at once.
    Switch user agents between queries
    Scrape randomly and don’t follow the same pattern
    Use intelligent IP rotations
    Clear Cookies after each IP change or disable them completely

Thanks for reading this blog post.

Source: http://blog.datahut.co/how-to-scrape-search-results-from-search-engines-like-google-bing-and-yahoo/