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.



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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.




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Professional Data Entry Services - Ensure Maximum Security for Data

Though a lot of people have concerns about it, professional data entry services can actually ensure maximum security for your data. This is in addition to the quality and cost benefits that outsourcing provides anyway. The precautionary measures for data protection would begin from the time you provide your documents/files for entry to the service provider till completion of the project and delivery of the final output to you. Whether performed onshore or offshore, the security measures are stringent and effective. You only have to make sure you outsource to the right service provider. Making use of the free trials offered by different business process outsourcing companies would help you choose right.

BPO Company Measures for Data Protection and Confidentiality

• Data Remains on Central Servers - The company would ensure that all data remains on the central servers and also that all processing is done only on these servers. No text or images would leave the servers. The company's data entry operators cannot download or print any of this data.

• Original Documents Are Not Circulated - The source files or documents (hard copies) which you give to the service provider is not distributed as such to their staff. This source material is scanned with the help of high speed document scanners. The data would be keyed from scanned images or extracted utilizing text recognition techniques.

• Source Documents Safely Disposed Of - After use, your source documents would be disposed of in a secure manner. Whenever necessary, the BPO company would get assistance from a certified document destruction company. Such measures would keep your sensitive documents from falling into the hands of unauthorized personnel.

• Confidentiality - All staff would be required to sign confidentiality agreements. They would also be apprised of information protection policies that they would have to abide by. In addition, the different projects of various clients would be handled in segregated areas.

• Security Checks - Surprise security checks would be carried out to ensure that there is adherence to data security requirements when performing data entry services.

• IT Security - All computers used for the project would be password protected. These computers would additionally be provided with international quality anti-virus protection and advanced firewalls. The anti-virus software would be updated promptly.

• Backup - Regular backups would be done of information stored in the system. The backup data would be locked away securely.

• Other Measures - Other advanced measures that would be taken for information protection include maintenance of a material and personnel movement register, firewalls and intrusion detection, 24/7 security manning the company's premises, and 256 bit AES encryption.

Take Full Advantage of It

Take advantage of professional data entry services and ensure maximum security for your data. When considering a particular company to outsource to, do ask them about their security measures in addition to their pricing and turnaround.




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Backtesting & Data Mining

In this article we'll take a look at two related practices that are widely used by traders called Backtesting and Data Mining. These are techniques that are powerful and valuable if we use them correctly, however traders often misuse them. Therefore, we'll also explore two common pitfalls of these techniques, known as the multiple hypothesis problem and overfitting and how to overcome these pitfalls.

Backtesting

Backtesting is just the process of using historical data to test the performance of some trading strategy. Backtesting generally starts with a strategy that we would like to test, for instance buying GBP/USD when it crosses above the 20-day moving average and selling when it crosses below that average. Now we could test that strategy by watching what the market does going forward, but that would take a long time. This is why we use historical data that is already available.

"But wait, wait!" I hear you say. "Couldn't you cheat or at least be biased because you already know what happened in the past?" That's definitely a concern, so a valid backtest will be one in which we aren't familiar with the historical data. We can accomplish this by choosing random time periods or by choosing many different time periods in which to conduct the test.

Now I can hear another group of you saying, "But all that historical data just sitting there waiting to be analyzed is tempting isn't it? Maybe there are profound secrets in that data just waiting for geeks like us to discover it. Would it be so wrong for us to examine that historical data first, to analyze it and see if we can find patterns hidden within it?" This argument is also valid, but it leads us into an area fraught with danger...the world of Data Mining

Data Mining

Data Mining involves searching through data in order to locate patterns and find possible correlations between variables. In the example above involving the 20-day moving average strategy, we just came up with that particular indicator out of the blue, but suppose we had no idea what type of strategy we wanted to test? That's when data mining comes in handy. We could search through our historical data on GBP/USD to see how the price behaved after it crossed many different moving averages. We could check price movements against many other types of indicators as well and see which ones correspond to large price movements.

The subject of data mining can be controversial because as I discussed above it seems a bit like cheating or "looking ahead" in the data. Is data mining a valid scientific technique? On the one hand the scientific method says that we're supposed to make a hypothesis first and then test it against our data, but on the other hand it seems appropriate to do some "exploration" of the data first in order to suggest a hypothesis. So which is right? We can look at the steps in the Scientific Method for a clue to the source of the confusion. The process in general looks like this:

Observation (data) >>> Hypothesis >>> Prediction >>> Experiment (data)

Notice that we can deal with data during both the Observation and Experiment stages. So both views are right. We must use data in order to create a sensible hypothesis, but we also test that hypothesis using data. The trick is simply to make sure that the two sets of data are not the same! We must never test our hypothesis using the same set of data that we used to suggest our hypothesis. In other words, if you use data mining in order to come up with strategy ideas, make sure you use a different set of data to backtest those ideas.

Now we'll turn our attention to the main pitfalls of using data mining and backtesting incorrectly. The general problem is known as "over-optimization" and I prefer to break that problem down into two distinct types. These are the multiple hypothesis problem and overfitting. In a sense they are opposite ways of making the same error. The multiple hypothesis problem involves choosing many simple hypotheses while overfitting involves the creation of one very complex hypothesis.

The Multiple Hypothesis Problem

To see how this problem arises, let's go back to our example where we backtested the 20-day moving average strategy. Let's suppose that we backtest the strategy against ten years of historical market data and lo and behold guess what? The results are not very encouraging. However, being rough and tumble traders as we are, we decide not to give up so easily. What about a ten day moving average? That might work out a little better, so let's backtest it! We run another backtest and we find that the results still aren't stellar, but they're a bit better than the 20-day results. We decide to explore a little and run similar tests with 5-day and 30-day moving averages. Finally it occurs to us that we could actually just test every single moving average up to some point and see how they all perform. So we test the 2-day, 3-day, 4-day, and so on, all the way up to the 50-day moving average.

Now certainly some of these averages will perform poorly and others will perform fairly well, but there will have to be one of them which is the absolute best. For instance we may find that the 32-day moving average turned out to be the best performer during this particular ten year period. Does this mean that there is something special about the 32-day average and that we should be confident that it will perform well in the future? Unfortunately many traders assume this to be the case, and they just stop their analysis at this point, thinking that they've discovered something profound. They have fallen into the "Multiple Hypothesis Problem" pitfall.

The problem is that there is nothing at all unusual or significant about the fact that some average turned out to be the best. After all, we tested almost fifty of them against the same data, so we'd expect to find a few good performers, just by chance. It doesn't mean there's anything special about the particular moving average that "won" in this case. The problem arises because we tested multiple hypotheses until we found one that worked, instead of choosing a single hypothesis and testing it.

Here's a good classic analogy. We could come up with a single hypothesis such as "Scott is great at flipping heads on a coin." From that, we could create a prediction that says, "If the hypothesis is true, Scott will be able to flip 10 heads in a row." Then we can perform a simple experiment to test that hypothesis. If I can flip 10 heads in a row it actually doesn't prove the hypothesis. However if I can't accomplish this feat it definitely disproves the hypothesis. As we do repeated experiments which fail to disprove the hypothesis, then our confidence in its truth grows.

That's the right way to do it. However, what if we had come up with 1,000 hypotheses instead of just the one about me being a good coin flipper? We could make the same hypothesis about 1,000 different people...me, Ed, Cindy, Bill, Sam, etc. Ok, now let's test our multiple hypotheses. We ask all 1000 people to flip a coin. There will probably be about 500 who flip heads. Everyone else can go home. Now we ask those 500 people to flip again, and this time about 250 will flip heads. On the third flip about 125 people flip heads, on the fourth about 63 people are left, and on the fifth flip there are about 32. These 32 people are all pretty amazing aren't they? They've all flipped five heads in a row! If we flip five more times and eliminate half the people each time on average, we will end up with 16, then 8, then 4, then 2 and finally one person left who has flipped ten heads in a row. It's Bill! Bill is a "fantabulous" flipper of coins! Or is he?

Well we really don't know, and that's the point. Bill may have won our contest out of pure chance, or he may very well be the best flipper of heads this side of the Andromeda galaxy. By the same token, we don't know if the 32-day moving average from our example above just performed well in our test by pure chance, or if there is really something special about it. But all we've done so far is to find a hypothesis, namely that the 32-day moving average strategy is profitable (or that Bill is a great coin flipper). We haven't actually tested that hypothesis yet.

So now that we understand that we haven't really discovered anything significant yet about the 32-day moving average or about Bill's ability to flip coins, the natural question to ask is what should we do next? As I mentioned above, many traders never realize that there is a next step required at all. Well, in the case of Bill you'd probably ask, "Aha, but can he flip ten heads in a row again?" In the case of the 32-day moving average, we'd want to test it again, but certainly not against the same data sample that we used to choose that hypothesis. We would choose another ten-year period and see if the strategy worked just as well. We could continue to do this experiment as many times as we wanted until our supply of new ten-year periods ran out. We refer to this as "out of sample testing", and it's the way to avoid this pitfall. There are various methods of such testing, one of which is "cross validation", but we won't get into that much detail here.

Overfitting

Overfitting is really a kind of reversal of the above problem. In the multiple hypothesis example above, we looked at many simple hypotheses and picked the one that performed best in the past. In overfitting we first look at the past and then construct a single complex hypothesis that fits well with what happened. For example if I look at the USD/JPY rate over the past 10 days, I might see that the daily closes did this:

up, up, down, up, up, up, down, down, down, up.

Got it? See the pattern? Yeah, neither do I actually. But if I wanted to use this data to suggest a hypothesis, I might come up with...

My amazing hypothesis:

If the closing price goes up twice in a row then down for one day, or if it goes down for three days in a row we should buy,

but if the closing price goes up three days in a row we should sell,

but if it goes up three days in a row and then down three days in a row we should buy.

Huh? Sounds like a whacky hypothesis right? But if we had used this strategy over the past 10 days, we would have been right on every single trade we made! The "overfitter" uses backtesting and data mining differently than the "multiple hypothesis makers" do. The "overfitter" doesn't come up with 400 different strategies to backtest. No way! The "overfitter" uses data mining tools to figure out just one strategy, no matter how complex, that would have had the best performance over the backtesting period. Will it work in the future?

Not likely, but we could always keep tweaking the model and testing the strategy in different samples (out of sample testing again) to see if our performance improves. When we stop getting performance improvements and the only thing that's rising is the complexity of our model, then we know we've crossed the line into overfitting.



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Data Mining Software - Discover Software Modernization

Data mining software is usually an application that one uses and covers mostly with one's knowledge in the discovery of software modernization. Mining data software involves the understanding of the software artifacts that exist and the mining data tools. This process has very close relations with reverse engineering. The knowledge that one gains from studying data software that exists is usually presented in forms of models and by doing these queries one can be in a position to make his personal data mining software. With the knowledge that someone gains it must be applicable and one must also know the mining data tools that are suppose to be used apart from the soft wares. One can be able to know very widely about the mining data tools that are there in mining data software by doing computer science as a course. Computer science covers widely on what are the procedures, steps of mining data software and how can use the mining data tools.

This software is mostly used in making of databases schemes. Making of databases is not as easy as many would think it requires one to have some knowledge about computer engineering and the basic concepts of computers.;This software is mostly used in data crawling because it can be in a position to store data and one can be able to retrieve the data when needed.

The softwares are not that cheap they come in different varieties and it will depend on which information or the database on which one is coming up with.

Data mining software are usually in different levels there is the data level, design level, application level, architectural level, call graph level and program level it will depend on which level one is covering and this come together with mining data tools.

Data software's have increased rapidly through the introduction of computers and ERP definition. Computers hackers have been able to get the softwares at a very low price and this has made data mining to become very easy and quick to use in the shops and supermarkets and also government institutions. One cannot do data crawling without having the basic knowledge about data mining soft wares because soft wares are the programmes that are usually installed into the computer and without the programmes then no data can be processed.

There are a lot of challenges that come with the use of the mining soft ware. One can easily crush the software he is using or the softwares can easily break they are normally sold on CDS one can easily break it or loose it.

High chances of losing the data that someone is coming up with is very high because computers easily crash due to some difficulties that they experience or a virus can easily crush the computer.

Mining software take a very large space and in most of the computers. The reason behind this is because, data crawling use graphics. Graphics usually occupy a lot of space in terms of the size of the local disk. One is suppose to look for a computer that has very good memory. Data crawling is something that needs to be updated each and every time something appears along the way.



Source: http://ezinearticles.com/?Data-Mining-Software---Discover-Software-Modernization&id=5054991

Should I Really Outsource My Data Entry?

What is Data Entry?

Data entry is the process of entering in various forms of data into an electronic format normally through a computer. As the world continues to advance in technology, more and more businesses are transitioning from the old school way of manual data management to relying on computers to empower these operational needs. Data entry services provide businesses the ability to transfer data from physical paper form into various electronic formats which then can be easily accessed through a computer. Among the different types of data management solutions businesses seek include inventory management, customer database management and sales record management, just to name a few.

Why outsource?

Data entry can be a very tedious and time consuming process. Many businesses today just do not have the time and budget to support such a tedious yet crucial process. Outsourcing your needs will not only save you on the cost of hiring employees internally, it will also help increase productivity. Most outsourcing companies have hundreds of operators and have the capability to run at full production capacity. Most companies just cannot support this if they hired internally. Turn around times can increase by as much as 300% by outsourcing your data entry needs and reduce cost up to 75% of normal domestic salary cost.

What to look for in an outsourcing company?

Probably the biggest concern with outsourcing your internal business processes is the quality of the services provided. When looking for a partner company to outsource to, make sure you choose a company with vast expertise and experience. Ask for previous clients they have worked with and ask for samples. A company that has been in the industry longer will have the resources and experience to provide you with a higher level of quality service.

Another concern of outsourcing is communication. Most outsourcing companies are located abroad in various parts of Asia and in different time zones. Communication problems could arise if you do not choose a company that has a process in place to deal with this. Some things to look out for are the availability of direct phone numbers, instant messaging support, and direct emails.



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The Benefits of Data Outsourcing

Data is the foundation of all companies and provides a source for multiplying your company with tremendous leaps and bounds. The benefits of data entry outsourcing are numerous with expansion of methodologies, which provide your business with many other numerous benefits. Data entry is a generalized term, which entails virtual services like data mining, data conversion, image processing, web data entry, data extraction and many others. All of these tasks are very much a stronghold in getting the processes of any company streamlined without wasting time and resources.

If you think about the benefits of data-entry outsourcing, it is necessary for any company to add the need of data entry along with other important resources, which go with this task. It is also important to know the format, which the final version of data is to be utilized. You want to go for data that is available for usage in a cross platform environment.

There are many benefits of outsourcing. In today's society, data-entry services offer peace of mine as well as a sigh of relief for business owners. Here are some benefits which data-entry outsourcing companies can offer your company:

o Your complete data management needs being taken care of. When you outsource your data-entry needs to an outside company, you are going to benefit with having managed and synchronized data. This will ensure that your company will save time. The best thing about outsourcing companies is that some of the managed data can be utilized for repository purposes.

o Time is very critical when dealing with competition. You want to get data in and out of your business in order to reap the maximum possible benefits in the least amount of time. Utilizing an outsourcing company minimizes your time spent while improving the efficiency of your business processes.

o Your sole purpose for utilizing data-entry outsourcing companies should be to receive quality as well as the most quantity for your dollar. Quality cannot be compromised. Quantity also needs to be delivered fast and on time. There is no leeway given when it comes to data entry work. Receiving data-entry work on time with fast turn around allows your business to benefit with the business overhead.

o Outsourcing companies are affordable and what does this mean for your business? This will reduce your business costs while maximizing your profits.



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