Technical presentation: Data Mining

The aim of Data Mining
The databases volume increase, the data access generalization, and the new analysis needs of business users, have developed the need for Data Mining tools. Beyond the classical data navigation products, business users now need an anlysis tool, that can perform guided and on-line analysis, following an interactive and user-friendly process, and giving easy to understand results.

With the development of data warehouses and storage solutions, the amount of data held by enterprises is becoming more and more important. Many pieces of information, which direct interest may not appear at first sight, are stored because it is now easy and inexpensive. This point explains why data mining has become a key issue in today information systems: when data mining is needed, the raw data necessary to process the analysis will almost always be there, waiting to reveal all the hidden trends it holds.

The need for data mining has developed since business users wanted to reappropriate their data, to easily test their models, and to build out new ones, guided by their strong knowledge of the business. One major improvement of data mining over classical navigation tools is that, further than just testing an already supposed trend, it can discover completely new patterns, and fully validate a model, with the business knowledge of the user. The model can be automatically built, but each step of its construction can be controled by the business user. This model then enables him to predict the behaviour of other pieces of data, and to evaluate their chances to behave as predicted.

But this work on the data is efficient only if it follows a good methodology. Several steps should be followed, in order to reach significant results.
Starting from heterogenous, raw data, you should first set up your data warehouse, in order to have a clean relational database.
You can then start the alimentation of your data mining tool. At this stage, you should be able to provide a good data description.
One key step is a good definition of your problematic. However powerfull it might be, a data mining tool needs that you isolate the question you want to answer. Even if this question can change during the analysis, a good starting point must be defined.
Then only you can reach the analysis steps. During the analysis, you will need to go back several times to a preparation step, where you can define new, more relevant variables, use aggregation formulas,... While developing your model, the need go back to the preparation step is likely to happen. During these steps, your business knowledge will be necessary, to keep interesting patterns isolated by the tool, or reject biased ones.
After your analysis, you will be able to report your conclusions, as an expert of the domain.

ISoft provides tools that are designed specifically for the business user. With ALICE d'ISoft, you will be assisted during these steps. This user friendly tool, with an intuitive interface, will guide you during all the analysis, performed as an on-line process.

Learn more about Decision Trees.

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