The need for specialized data mining techniques

Interactive exchange of information as well as collaboration. Where an example is wikis and blogs, and which is now also extend to other areas data mining techniques. These new pages are the result of new technologies and new ideas and are at the forefront of web development. Because of their novelty, they create quite an interesting challenge for data mining. info technology hub

Data mining is simply a process of finding data mining techniques

Data mining is simply a process of finding patterns in data masses. There is so much information on the web that you need data mining tools to make sense. Traditional data mining techniques are not very effective. When use on these new Web 2.0 sites because the user interface is so varie. Since Web 2.0 sites are largely built with user-supply content, there is even more data. That needs to be mine to gain valuable insight.

The need for specialized data mining techniques

That said, the extra freedom of the format ensures. That it’s much harder to say through the content to find what’s useful. The available data is very valuable, so where there is a new platform. New techniques for mining the data must be develope. The trick is that the data mining methods themselves need to be flexible, as the sites they target are flexible. In the early days of the World Wide Web, known as Web 1.0, data mining programs knew where to look for the information they wanted. Web 2.0 sites lack structure, which means there is no place to point to the mining program. You need to be able to scan and browse all user-generate content to find what is neede.

The advantage is that there is much mining techniques

The advantage is that there is much more available data. Which means increasingly accurate results if the data can be used correctly. The downside is that with all this data, if the selection criteria are not specific enough, the results do not make sense. Too good is definitely a bad thing. Wikis and blogs have been around long enough now that enough research has is do to better understand them. This research can now again be used to design the best possible data mining methods. New algorithms are being developed that enable data mining applications to analyze this data and be useful. Another problem is that there are now many dead ends on the internet where groups of people share information freely, but only behind walls / barriers that keep it away from the overall results.

The biggest challenge in developing mining techniques

The biggest challenge in developing these algorithms is not finding data because there are too many. The challenge is to filter out irrelevant data to get to the most meaningful ones. At this point, none of the techniques are perfecte. This makes Web 2.0 data mining an exciting and frustrating field, and yet another challenge in the endless series of technological obstacles. That have come out of the Internet. There are many problems to solve.

One is the inability to rely on keywords, which use to be the best search method. This does not allow understanding of the context or mood associated with keywords. That can drastically change the meaning of the keyword population. Social networking sites are a great example of this where you can share information with everyone you know. But it’s harder for that information to spread outside these circles. This is good in terms of privacy, but it does not add to. The collective knowledge base and can lead to bias.

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