Updated March 22, 2023
Introduction to Types of Data Mining
The term “Data Mining” means that we need to look into a large dataset and mine data out of the same to portray the essence of what data wants to say. Very similar to how coal mining is done, where coal deep beneath the ground is mined using various tools, data mining also has associated tools for making the best out of the data. One widespread misinterpretation of data mining is that it is thought about as something where we try to extract new data, but it is not always true. It also refers to something where we try to get meaning out of the data we already have. Thus, data mining in itself is a vast field wherein we will deep dive into specifically the tools in Data Mining in the next few paragraphs. In this article, we will discuss the Types of Data Mining.
What is Data Mining?
As talked about data mining earlier, data mining is a process to bring the best out of the data. The tools of data mining act as a bridge between the data and information from the data. In a few blogs, data mining is also termed Knowledge discovery. Here we would like to give a brief idea about the data mining implementation process so that the intuition behind the data mining is clear and becomes easy for readers to grasp. Below the flowchart represents the flow:
In the process discussed above, there are tools at each level, and we would try to take a deep dive into the most important ones.
Types of Data Mining
Data mining can be performed on the following types of data:
1. Smoothing (Prepare the Data)
This particular method of data mining technique comes under the genre of preparing the data. The main intent of this technique is removing noise from the data. Here algorithms like simple exponential, the moving average are used to remove the noise. During exploratory analysis, this technique is convenient to visualize trends/sentiments.
2. Aggregation (Prepare the Data)
As the term suggests, a group of data is aggregated to achieve more information. This technique is employed to overview business objectives and can be performed manually or using specialized software. This technique is generally employed on big data, as big data don’t provide the required information as a whole.
3. Generalization (Prepare the Data)
Again, as the name suggests, this technique is employed to generalize data as a whole. This is different from aggregation, so the data during generalization is not grouped to achieve more information, but the entire data set is generalized. This will enable a data science model to adapt to newer data points.
4. Normalization (Prepare the Data)
In this technique, special care is employed to data points to bring them into the same scale for analysis. For example, a person’s age and salary fall in different measurement scales; hence plotting them on a graph won’t help us attain any useful info about the trends present as a collective feature. Using normalization, we can bring them into an equal scale to perform apple to apple comparison.
5. Attribute/Feature selection (Prepare the Data)
In this technique, we employ methods to select features so that the model used to train the data sets can imply value to predict the data it has not seen. This is very analogous to choosing the right outfit from a wardrobe full of clothes to fit oneself right for the event. Non-relevant features can negatively impact model performance, let alone improving performance.
6. Classification (Model the Data)
In this technique of data mining, we will group know as “classes”. In this technique, we employ the features selected (as discussed in the above point) collectively to groups/categories. For example, in a shop, if we have to evaluate whether a person will buy a product or not, there are “n” number of features we can collectively use to get a result of True/False.
7. Pattern Tracking
This is one of the basic techniques employed in data mining to get information about trends/patterns which the data points might exhibit. For example, we can determine a trend of more sales during a weekend or holiday time rather than on weekdays or working days.
8. Outlier Analysis or Anomaly Detection
Here and the name suggests, this technique is used to find or analyse outliers or anomalies. Outliers or anomalies are not negative data points; they are just something that stands out from the entire dataset’s general trend. On identifying the outliers, we can either remove them completely from the dataset, which occurs when data preparation is done. Or else this technique is extensively used in model datasets to predict outliers as well.
9. Clustering
This technique is pretty much similar to classification, but the only difference is we don’t know the group in which data points will fall post grouping after collection of features. This method is typically used in grouping people to target similar product recommendations.
10. Regression
This technique is used to predict the likelihood of a feature with the presence of other features. For example, we can formulate the likelihood of an item’s price concerning demand, competition, and a few other features.
11. Neural Network
This technique is based on the principle of how biological neurons work. Similar to what neurons in the human body does, the neurons in a neural network in data mining work also acts as the processing unit and connecting another neuron to pass on the information along the chain.
12. Association
In this data mining method, the relation between different features is determined and, in turn, used to find either hidden patterns or related analysis is performed as per business requirement. For example, we can find features correlated to each other using the association and thus emphasize removing anyone to remove some redundant features and improve processing power/time.
Conclusion
To conclude, there are different requirements one should keep in mind while data mining is performed. One needs to be very careful of what the output is expected to be so that corresponding techniques can be used to achieve it. Though data mining is an evolving space, we have tried to create an exhaustive list of all types of data mining tools above for readers.
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