Data Mining Is Also Known As Knowledge Discovery

With the hit of the era of information technology, we have incorporated into an ocean of information. This information explosion is based heavily on the Internet, which has become one of the global information infrastructures. We can not the fact that every day that goes deny the content of the website are growing by leaps information and as such, it becomes increasingly difficult to obtain the desired information we are really looking.

Web mining is a tool that can be used in customizing Web sites based on content and also by the user. Web mining includes normal use of the contents of mines and structure. Data mining, text mining and web mining, different techniques and procedures to provide the necessary information to the huge database, so that companies can make better business decisions with precision, therefore, data mining, text mining and Web mining helps a lot in promoting.

The objectives of the “customer relationship management”, whose main objective is to initiate, develop relationships with customers and adapt through profiling and classification of customers. However, there are many issues to be addressed while dealing with the process of exploring the web. Confidentiality can be said of the trigger-button issue. Recently, complaints and breaches of confidentiality concerns considerably, as traders, businesses and governments continue to collect and store the vast amount of private information.

There are concerns not only the collection and compilation of private information, but also the analysis and use of such data. Fuelled by public concern about the increasing amount of statistics and effective technologies consisting of: conflict between confidentiality and mining is probably the basis for the higher levels of inspection in the coming years. Legal disputes are also likely in this regard. There are other problems that data mining. “Incorrect information” may lead us to vague and inaccurate analysis results and recommendations.

The submission of false information or false information when importing customer data creates a real danger to the effectiveness of web mining and effectiveness. Another risk in data mining is the mining industry may be confused with data warehousing. Companies developing data warehouses without using the correct browser software are less likely at the level of accuracy and efficiency to reach and they are also less likely to take full advantage.

Similarly, cross-selling cause a problem if it breaks the privacy of clients, the violation of their faith or bored with unnecessary stress. Web mining can be a great help to improve and link marketing programs that focus on the customers’ interests and needs. Despite the potential obstacles and barriers, the market for the exploration of the web to increase by several billion dollars in the coming years.

Mining can identify and target potential customers, what information is “buried” in the vast databases and strengthen customer relationships. Data mining tools can not predict future market trends and consumer behavior, which may help companies make resolutions and proactive, knowledge-based. This is one reason why data mining known as “Knowledge Discovery”.

It can be said of the process of analyzing data from different perspectives and sorting and grouping data identified and finally to a database of useful information, which can still be analyzed and used by companies to establish and enhance revenue generation and cost reduction. With the use of data mining, are business organizations find it easier to ask about the ability of companies and intelligence, which were very complicated and complex to analyze and determine the earliest response.
 

Joseph Hayden writes article on Web Data Extraction, Data Extraction Services, Website Data Extraction, Web Screen Scraping, Web Data Scraping, Web Data Extraction etc.

Submit a Comment

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>