SIZ Approved Course | Data Mining |Lecture 02| Stages of Data mining Process/Data Mining Techniques. By @goodybest

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welcome to My blog! I hope everything is going on well with everyone, as for me I'm great and it's with great joy I present to you all my second lecture on Data Mining. I believe you can still recall that I treated what data mining is, objective and importance of data mining and application of data mining in our Last lecture. So today I will be discussing with you how data mining works, stages of data mining process and data mining techniques so stay tune!.

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Data Mining tells us what is going to happen next. The techniques use to carry out this purpose is known as modelling. Modelling is the act of making a model or a representation in one situation that you know, and apply that same model in the situation that you don't know.
Data mining software analyzes relationships and patterns. We have many types of analytical software such as statistical, machine learning and neutral networks.

The Four Types Of Relationship Which Are Required In Data Mining:

• Classes: stored data is used to trace data in pre-arranged groups.
• Cluster: data items are group on the basis of logical relationships or customer preferences.
• Associations: this is also called Associative mining. data can also be extracted to identify associations.
• Sequential Patterns: data is extract to predict behaviour patterns and trends.
  

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• Data Gathering: first of all we have to collect and analyze data.
• Data Cleaning: this is the second stage. data cleaning is discovering and correcting the inaccurate data.
• Feature Extraction: In feature extraction we get only the important characteristic of the Data.
• Pattern extraction and Discovery: This stage is often called as "Data Mining". And this is the most important stage of data mining where we have to put our all efforts in Order to reveal something new.
• Visualization of result: data visualisation is to let the user understand what is going on. Data Mining usually involves the reduction (extraction) of hidden data from database that the user did not already know about.
• Evaluation Of Result: this is the last stage of this process and this stage we closely observed the given data and find out the correct result.

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Basically we have two data mining techniques

• Predictive
• Descriptive

Predictive Data Mining: predictive data mining uses historical data to predict about future theories. It merges database analysis with artificial intelligence. Predictive data mining is further categorised into:

• Classification
• Regression.

Descriptive data mining descriptive data mining is to find patterns in the data, they are generally used to create meaningful subgroups descriptive data mining his further classified into:

• Clustering
• Association
• Sequential analysis.
  

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Data mining can be used in any organization that needs to find patterns or relationships in their data and it is increasing rapidly. It can be used in every kind of business. This is where we will stop for this week. We've learned the proper way Data Mining works; which is the use of the different Data Mining software available, we also learn the 6 different stages of data mining ⛏️ and finally we've come to know the Data mining techniques. In our next lecture I will focus on data warehousing and data Mart. Please additional points and comments is welcome ☺️ Thanks for reading through!
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