Saturday, June 27, 2009

Data Mining (finding information)

Hello guys,

Hard financial times are common issue for every company today, so being profitable our company is vital as life or death? Well Data mining can help you up, let me comment a little bit about this knowledge process, remember it does not depend of the business size.

As you know, Every organized company keep electronic records about sales, customers, inventory, and most of them are using only for IRS purposes or internal audit. Well this is the real input for this powerful process.

Data mining is the process of extracting hidden patterns from data, it can answer us questions like: what are my best mix-products ?, might be our marketing department is not focusing in this products. Our products being still in their maturity cycle? or is time no renew them or take them off the market.

Well , the following methodology works.

  • Meta data - Look up you data and transform y consistent values without errors.

  • Classification - Arranges the data into predefined groups.

  • Clustering - Is like classification but the groups are not predefined, so the algorithm will try to group similar items together.

  • Regression - Attempts to find a function which models the data with the least error. A common method is to use Genetic Programming.

  • Association rule learning - Searches for relationships between variables. For example a
    supermarket might gather data of what each customer buys. Using association rule learning, the supermarket can work out what products are frequently bought together, which is useful for marketing purposes. This is sometimes referred to as "market basket analysis".

  • Results validation The final step of knowledge discovery from data is to verify the patterns produced by the data mining algorithms occur in the wider data set.

Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set, this is called over fitting. If the learn patterns do not meet the desired standards, then it is necessary to reevaluate and change the preprocessing and data mining. If the learnt patterns do meet the desired standards then the final step is to interpret the learnt patterns and turn them into knowledge.

Well, Hopefully you founded interesting this article, if you have any comment please write it down, I really appreciated it. ftbizhouston also can help you in your data mining trip.

Good luck,


No comments:

Post a Comment