Data Mining Techniques Tutorial: 5 Best Data Mining Techniques Data Analyst Must Know

Data Mining is an analytic procedure intended to process data – usually an enormous data– to look for predictable patterns or potential methodical relationships amongst the data. Then these pattern discoveries can be used to extract new knowledge and insights by applying the identified patterns to new subsets of data.

In simple words, an ultimate objective of mining the data is extracting the relevant knowledge from the data. Therefore, data mining is sometimes also called ‘knowledge discovery’.

How Data Mining Works?

Data mining techniques utilize complex mathematical algorithms to break down the information and assess the likelihood of future events.

Data mining methods can be performed from any source in which data is saved like spreadsheets, flat files, database tables, or any other storage format. The crucial criteria for the information are not the format of the storage, but rather its relevance to the issue to understand it well.

Appropriate data cleansing and arrangement are essential for mining the data. Data mining may use a number of techniques including machine learning, database management, statistical analysis etc.

Here are some of the best data mining techniques:

1. Prediction

Prediction is amongst the most common techniques for mining the data since it’s utilized to forecast the future scenarios based on the current and new data.  In predictive data mining – existing & historical data is analysed to identify patterns. Once the patterns are analysed – new data is then fed to these patterns to forecast the future scenarios.

Predictive data mining is the most widely recognized class of mining process because it has the most immediate business applications.

2. Classification

Classification is another important technique for data mining. In classification – different techniques are used to classify the data into predefined segments or classes. Classification uses complicated techniques for mining the data to extract different attributes together into clear discernible classes. Classification then would employ techniques and algorithms to the new data to decide to which class this data should belong to.

One of the most common example is how Gmail classes the new emails into spam or not spam based on different attributes of the email.

3. Regression

Regression analysis is a procedure of recognizing and breaking down the relationship amongst the different variables.  In simple words – regression is a technique to predict various possible outcomes in different scenarios. Outcome that is needed to be predicted is a dependent variable dependent on scenarios or independent variables.

4. Clustering

Clustering analysis is the technique used to distinguishing data sets that are like one another, to comprehend the similarities and distinctions in the existing and new data. Clusters share certain similar features that can be utilized to develop targeting algorithms. For instance, clusters of buyers with comparable purchasing behavior can be targeted with the same products/services to boost the conversation rate and sales.

An outcome from a clustering analysis technique can be the formation of personas created to represent the distinctive customer types in a targeted statistic, demeanor or potential behavior set that may utilize a product, brand, or website correspondingly.

Difference between clustering and classification is that while in classification there are predefined classes, in clustering the clusters or classes evolve from the data after it is mined.

5. Association Rule

Association rule detection is a critical interpretive strategy in the data mining and analysis process. This method finds the relationship between at least two products. It sees the concealed patterns in the data sets which is utilized to recognize the variables and the continuous event of multiple variables that show up with the most significant frequencies.

It’s a fundamental technique; however, you’d be astonished how much knowledge and understanding it can give — the sort of data numerous organizations uses once a day to enhance effectiveness and generate more revenue.

Conclusion

Data mining is the amalgamation of statistics, database management, artificial intelligence,  machine learning technologies and data visualization. Data mining profession is all about solving this equation : how to prepare and form judgments from vast amounts of data.

All the above data mining methods can help in analyzing the unique information from alternate points of view. Right technique to use depends on the nature of the problem in hand and availability of the relevant data. It is about employing the relevant techniques from the available ones that can help you extract the most from the data that has been gathered.