How to Create Optimal Results Using 5 Data Mining Techniques
Originally published: 26/01/2018 13:03
Publication number: ELQ-28831-1
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How to Create Optimal Results Using 5 Data Mining Techniques

5 Data Mining Techniques to help you gather insights from big data.


Are you starving to gain insights from big data, but not sure what data mining techniques to use? Then read on.

Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that you’re trying to solve, will determine the type of data mining technique that will yield the best results.

In today’s digital world, we are surrounded with big data that is forecasted to grow 40%/year into the next decade.. The ironic fact is, we are drowning in data but starving for knowledge. Why? All this data creates noise which is difficult to mine – in essence we have generated a ton of amorphous data, but experiencing failing big data initiatives. The knowledge is deeply buried inside. If we do not have powerful tools or techniques to mine such data, it is impossible to gain any benefits from such data.


  • Step n°1 |


    This analysis is used to retrieve important and relevant information about data, and metadata. It is used to classify different data in different classes. Classification is similar to clustering in a way that it also segments data records into different segments called classes. But unlike clustering, here the data analysts would have the knowledge of different classes or cluster. So, in classification analysis you would apply algorithms to decide how new data should be classified.A classic example of classification analysis would be our Outlook email. In Outlook, they use certain algorithms to characterize an email as legitimate or spam.

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  • Step n°2 |


    It refers to the method that can help you identify some interesting relations (dependency modeling) between different variables in large databases. This technique can help you unpack some hidden patterns in the data that can be used to identify variables within the data and the concurrence of different variables that appear very frequently in the dataset.Association rules are useful for examining and forecasting customer behavior. It is highly recommended in the retail industry analysis. This technique is used to determine shopping basket data analysis, product clustering, catalog design and store layout. In IT, programmers use association rules to build programs capable of machine learning.

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  • Paya Danesh(last updated: 25/02/2018 09:46)


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