Data Mining As a Process

The data mining process is also known as knowledge discovery. It can be defined as the process of analyzing data from different perspectives and then summarizing the data into useful information in order to improve the revenue and cut the costs. The process enables categorization of data and the summary of the relationships is identified. When viewed in technical terms, the process can be defined as finding correlations or patterns in large relational databases. In this article, we look at how data mining works its innovations, the needed technological infrastructures and the tools such as phone validation.

Data mining is a relatively new term used in the data collection field. The process is very old but has evolved over the time. Companies have been able to use computers to shift over the large amounts of data for many years. The process has been used widely by the marketing firms in conducting market research. Through analysis, it is possible to define the regularity of customers shopping. How the items are bought. It is also possible to collect information needed for the establishment of revenue increase platform. Nowadays, what aides the process is the affordable and easy disk storage, computer processing power and applications developed.

Data extraction is commonly used by the companies that are after maintaining a stronger customer focus no matter where they are engaged. Most companies are engaged in retail, marketing, finance or communication. Through this process, it is possible to determine the different relationships between the varying factors. The varying factors include staffing, product positioning, pricing, social demographics, and market competition.

A data-mining program can be used. It is important note that the data mining applications vary in types. Some of the types include machine learning, statistical, and neural networks. The program is interested in any of the following four types of relationships: clusters (in this case the data is grouped in relation to the consumer preferences or logical relationships), classes (in this the data is stored and finds its use in the location of data in the per-determined groups), sequential patterns (in this case the data is used to estimate the behavioral patterns and patterns), and associations (data is used to identify associations).

In knowledge discovery, there are different levels of data analysis and they include genetic algorithms, artificial neural networks, nearest neighbor method, data visualization, decision trees, and rule induction. The level of analysis used depends on the data that is visualized and the output needed.

Nowadays, data extraction programs are readily available in different sizes from PC platforms, mainframe, and client/server. In the enterprise-wide uses, size ranges from the 10 GB to more than 11 TB. It is important to note that two crucial technological drivers are needed and are query complexity and, database size. When more data is needed to be processed and maintained, then a more powerful system is needed that can handle complex and greater queries.

With the emergence of professional data mining companies, the costs associated with process such as web data extraction, web scraping, web crawling and web data mining have greatly being made affordable.



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Is Web Scraping Relevant in Today's Business World?

Different techniques and processes have been created and developed over time to collect and analyze data. Web scraping is one of the processes that have hit the business market recently. It is a great process that offers businesses with vast amounts of data from different sources such as websites and databases.

It is good to clear the air and let people know that data scraping is legal process. The main reason is in this case is because the information or data is already available in the internet. It is important to know that it is not a process of stealing information but rather a process of collecting reliable information. Most people have regarded the technique as unsavory behavior. Their main basis of argument is that with time the process will be over flooded and therefore lead to parity in plagiarism.

We can therefore simply define web scraping as a process of collecting data from a wide variety of different websites and databases. The process can be achieved either manually or by the use of software. The rise of data mining companies has led to more use of the web extraction and web crawling process. Other main functions such companies are to process and analyze the data harvested. One of the important aspects about these companies is that they employ experts. The experts are aware of the viable keywords and also the kind of information which can create usable statistic and also the pages that are worth the effort. Therefore the role of data mining companies is not limited to mining of data but also help their clients be able to identify the various relationships and also build the models.

Some of the common methods of web scraping used include web crawling, text gripping, DOM parsing, and expression matching. The latter process can only be achieved through parsers, HTML pages or even semantic annotation. Therefore there are many different ways of scraping the data but most importantly they work towards the same goal. The main objective of using web scraping service is to retrieve and also compile data contained in databases and websites. This is a must process for a business to remain relevant in the business world.

The main questions asked about web scraping touch on relevance. Is the process relevant in the business world? The answer to this question is yes. The fact that it is employed by large companies in the world and has derived many rewards says it all. It is important to note that many people regarded this technology as a plagiarism tool and others consider it as a useful tool that harvests the data required for the business success.

Using of web scraping process to extract data from the internet for competition analysis is highly recommended. If this is the case, then you must be sure to spot any pattern or trend that can work in a given market.



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Understanding Data Mining

Well begun is half done. We can say that the invention of Internet is the greatest invention of the century which allows for quick information retrieval. It also has negative aspects, as it is an open forum therefore differentiating facts from fiction seems tough. It is the objective of every researcher to know how to perform mining of data on the Internet for accuracy of data. There are a number of search engines that provide powerful search results.

Knowing File Extensions in Data Mining

For mining data the first thing is important to know file extensions. Sites ending with dot-com are either commercial or sales sites. Since sales is involved there is a possibility that the collected information is inaccurate. Sites ending with dot-gov are of government departments, and these sites are reviewed by professionals. Sites ending with dot-org are generally for non-profit organizations. There is a possibility that the information is not accurate. Sites ending with dot-edu are of educational institutions, where the information is sourced by professionals. If you do not have an understanding you may take help of professional data mining services.

Knowing Search Engine Limitations for Data Mining

Second step is to understand when performing data mining is that majority search engines have filtering, file extension, or parameter. These are restrictions to be typed after your search term, for example: if you key in "marketing" and click "search," every site will be listed from dot-com sites having the term "marketing" on its website. If you key in "marketing site.gov," (without the quotation marks) only government department sites will be listed. If you key in "marketing site:.org" only non-profit organizations in marketing will be listed. However, if you key in "marketing site:.edu" only educational sites in marketing will be displayed. Depending on the kind of data that you want to mine after your search term you will have to enter "site.xxx", where xxx will being replaced by.com,.gov,.org or.edu.

Advanced Parameters in Data Mining

When performing data mining it is crucial to understand far beyond file extension that it is even possible to search particular terms, for example: if you are data mining for structural engineer's association of California and you key in "association of California" without quotation marks the search engine will display hundreds of sites having "association" and "California" in their search keywords. If you key in "association of California" with quotation marks, the search engine will display only sites having exactly the phrase "association of California" within the text. If you type in "association of California" site:.com, the search engine will display only sites having "association of California" in the text, from only business organizations.

If you find it difficult it is better to outsource data mining to companies like Online Web Research Services



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Advantageous Data Entry Services in Era of Globalization

Data generally represent the information and can be defined with numbers or alphabetical symbols. Data entry can be determined as process that converts data from one form to another one. Such solutions usually includes almost all business fields and professional services, such as data conversion, offline data entry work, data processing, image processing, data entry outsourcing, data mining etc. One has to collect data on various topics and have to represent them in some meaningful manner.

There are several tasks for data entry services. It may includes data-entry into websites, tracking debit or credit card transactions, entry into electronic books, image formatting, keeping hard copy of office applications for scanning or printing, database for mails, use of data entry software as well as management of all these activities. In addition some time consuming tasks such as entering data in offline mode to track websites, gathering effective websites, which may need for consultation and to fill online forms. One of the good examples of data entry tasks is writing the image. You have to enter the images to incorporate pictures and attachments in magazines, e Books and white papers. Scanned images also needed to enter the details on the file. Another example of data-entry work is insurance claim. Insurance firms file a claim for insurance in process to get the cost of services. All systems for payment, form processing and insurance claims are followed by data entry services.

Data processing is also very useful tasks needed to be managed, regardless of company size or complexity. You have to follow some methods in order to accomplish your data processing tasks accurately. Such services help firms in terms of clear analysis of activities, policies, strategies and actions. Data processing and other services like data cleaning, image processing, OCR clean up, survey processing are related to provide a well-processed and complete data which can be used to get simple explanation of data.

There are plenty of advantages such services. For example data conversion is process which is very significant for any firm to drive their business powerfully. Data conversion can be considered as transfer of data from one format to another. There are also some other useful services like data transformation and many other which directly or indirectly essential for smooth functionality of any business.

Be advantageous in this competitive environment by choosing the right business services for benefits of yours and your organization.



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Benefits of Online Data Entry

There is no doubt that data entry is a vital part of business development because no organized activity can take place without the organized manipulation of data. But, even though the correct manipulation of data is at the heart of any enterprise, it is also true that this is an activity that is time consuming and repetitive. In the past, businesses had to dedicate a good portion of their work hours for the mind-numbing job of feeding data. However, with the arrival of the internet, there have been revolutionary changes in the way business handle data.

These days, a good volume of data entry work is being carried out online. Many companies, regardless of their size, are outsourcing this kind of work. This is more so in developed countries like the US, UK and Europe. These countries outsource such work to a pool of educated youth working from developing nations like China, India and Malaysia. The Internet is what makes this possible. Since the job is being done in a different country where currency rates are relatively low, the parent company is able to cut costs. More importantly, many companies have begun to realize that the time and effort they are putting into data entry could be routed to processes that will help the business grow. Outsourcing frees the resources in the parent company, which in turn allows the parent company to dedicate more time and energy to improving their core competency.

Reputed outsourcing partners hire well educated people who have the technical expertise, the knowledge and the language skills to handle data entry jobs efficiently and reliably. By allowing these experts to streamline the data entry process, businesses get two benefits at once. On the one hand, they cut costs and improve their bottom lines. On the other hand, they hire a dedicated team of professionals who specialize in data entry jobs. Thus, there is no compromise in quality. In fact, they can expect good results from reputed outsourcing partners because of the excellent remuneration they receive. The differences in foreign currencies make outsourcing a lucrative source of income for many companies.

However, it is important to remember that online data entry is fraught with risks. The first and probably the only element of risk is the quality of the service provider. Choose an outsourcing partner who has sufficient experience in the field and has a solid reputation servicing international clients. In case the outsourcing company has a branch in the same geographical location, it becomes easier to liaise with them. Experienced outsourcing companies already have enough exposure to the needs of growing companies and can be trusted to deliver excellent quality and within prescribed time limits.


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One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.



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Web Mining - Applying Data Techniques

Web mining refers to applying data techniques that discover patterns that are usually on the web. Web mining comes in three different types: content mining, structure mining and usage mining, each and every technique has its significance and roles it will depend on which company someone is.

Web usage mining

Web usage mining mainly deals with what users are mainly searching on the web. It can be either multimedia data or textual data. This process mainly deals with searching and accessing information from the web and putting the information into a one document so that it can be easily be processed.

Web structure mining

Here one uses graphs and by using graphs one can be able to analyze the structure and node of different websites how they are connected to each other. Web structure mining usually comes in two different ways:

One can be able to extract patterns from hyperlinks on different websites.

One can be able to analyze information and page structures which will describe XML and HTML usage. By doing web structure mining one can be able to know more about java script and more basic knowledge about web design.

Advantages

Web mining has many advantages which usually make technology very attractive and many government agencies and corporations use it. Predictive analysis ones does not need a lot of knowledge like in mining. Predictive analytics usually analyze historical facts and current facts about the future events. This type of mining has really helped ecommerce one can be able to do personalize marketing which later yield results in high trade volumes.

Government institutions use mining tools to fight against terrorism and to classify threat. This helps in identifying criminals who are in the country. In most companies is also applicable better services and customer relationship is usually applied it gives them what they need. By doing this companies will be able to understand the needs of customers better and later react to their needs very quickly. By doing this companies will be able to attract and retain customers and also save on production cost and utilize the insight of their customer requirements. They may even find a customer and later provide the customer with promotional offers to the customer so that they can reduce the risk of losing the customer.

Disadvantages

The worst thing that is a threat to mining is invasion of privacy. Privacy in is usually considered lost when documents of one person is obtained, disseminated or used especially when it occurs without the presence of the person who came up with the data itself. Companies collect data for various reasons and purposes. Predictive analytics is usually an area that deals mainly with statistical analysis. Predictive analytics work in different ways deal with extracting information from the data that is being used and it will predict the future trends and the behavior patterns. It is vital for one to note that that accuracy will depend on the level of the business and the data understanding of the personal user.


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