Swiss data mining: the 4 techniques to know

Swiss data mining is a rather complicated topic that we will address today, however you should know that to do so you can enlist the support of experts in the field such as GROT.

In previous articles we told you about the Ticino company reputation and especially what it means anonymising data in Ticino.

Today, too, we will therefore address this issue with the extraction of Swiss data.

Let us therefore see in detail what this article is about

1.Extraction of Swiss data significance

2. The techniques used

3. Software used

4.Conclusions

Swiss data extraction significance

Swiss data mining is in great demand nowadays because it helps companies study how they can increase sales of their products or services.

For example, with the data of a purchase provided by a customer, one can understand gender, age, profession and much more information.

Thanks to Swiss data mining, the company will be able to find out which type of customer buys its different products or services.

Thus, we can say that Swiss data mining or data mining is considered to be that set of techniques and methodologies that have as their object the extraction of useful information in large amounts of data.

This data can be taken, for example, from databases through a variety of automatic or semi-automatic methods, such as machine learning.

To give you a better understanding of this concept, we could look at statistics, which we could thus define as the extraction of useful information from a set of data.

Here, the concept of Swiss data mining is very similar but has one major difference, namely that statistics can be used to process general population information, whereas Swiss data mining can be used to look for correlations between different variables relating to individuals.

For example, if you know the average behaviour of a regular customer, you will try to predict how much this customer will be able to spend in the immediate future.

Thus, Swiss data extraction is mathematical analysis, performed on large databases, which is preceded by other data preparation, transformation and filtering steps such as data cleaning.

We can say that data mining became very popular at the end of the 1990s and has a dual purpose, that of extraction with state-of-the-art analytical techniques and that of data exploration.

The latter is performed, as we said, automatically or semi-automatically on large amounts of data in order to discover patterns, i.e. significant patterns or regularities.

This Swiss data mining activity is used in many areas, from scientific research to market research.

It is also used to solve diverse problems that can range from customer relationship management (CRM), to the detection of fraudulent behaviour and even in website optimisation.

The techniques used

We can say that there are three main factors that have contributed to the development of Swiss data mining:

• large accumulations of data in electronic format;
• the data storage inexpensive;
• new analysis methods and techniques (machine learning).

This is because data mining techniques are based on specific algorithms.

As we have said, the patterns that are identified can themselves be starting points for hypothesising and verifying new causal relationships between phenomena.

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We can say that among the Swiss data mining techniques that are used the most are:

• Clusteringwhich is a technique for partitioning a data set into clusters or groups of objects. Clustering is performed using algorithms. Clustering methods identify similar or different data and feature analysis is performed.
• Neural networks; we can say that a simplification of biological neural networks has inspired computer science and the so-called artificial neural networks. 
• Decision treesHere we use what is called learning by classification. That is, starting from a well-defined set of classification examples for known cases, from which one expects to deduce a way of classifying them.
• Analysis of associations (identification of jointly purchased products).

Another technique that is very popular for data mining is what is called thelearning by classification.

The software used

There are several software packages that can be used to extract Swiss data, some of which are:

• SPSS: SPSS Clementine
• SAS: SAS Enterprise Miner and SAS Text Miner
• STATE
• R
• Oracle Data Miner
• Microsoft SQL Server: data mining tools of the DBMS produced by Microsoft
• Weka, datamining in Java
• RapidMiner

Conclusions

In conclusion, Swiss data mining is very useful for several reasons, which we will summarise below.

• They support large data sets: extractions with billions of rows of data can be created.
• They help to improve performance: when interacting with views that use extraction data sources, performance is generally better than with views linked to the original data.
• They support additional functionality: extractions allow you to use features of Tableau that are not available or supported by the original data, such as the ability to count unique values.
• They allow offline access to data: finally, if you use Tableau Desktop, extractions allow you to save and use the data locally if the original data is not available. How to work while travelling.

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