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, the data mining also has associated tools for making the best out of the data. One very common misinterpretation with data mining is that, it is thought about as something where we try to extract new data, but not always it is 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 the next few paragraphs we will deep dive into specifically the tools in Data Mining. 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 where we try to bring out 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 as 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 very handy 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 give an overview of 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.
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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 in a way the data during generalization is not grouped to together to achieve more information but in turn, 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 so as to bring them into the same scale for analysis. For example, the age and salary of a person 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 so that apple to apple comparison can be performed.
5. Attribute/Feature selection (Prepare the Data)
In this technique, we employ methods to perform a selection of 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 deal will groups 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 might be exhibited by the data points. 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 as well as the name suggests, this technique is used for finding or analyzing outliers or anomalies. Outliers or anomalies are not negative data points, they are just something that stands out from the general trend of the entire dataset. On identifying the outliers, we can either remove them completely from the dataset, which occurs when the preparation of data is done. Or else this technique is extensively used in model datasets to predict outliers as well.
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.
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 the price of an item with respect to 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.
In this method of data mining, the relation between different features are determined and in turn, used to find either hidden patterns or related analysis is performed as per business requirement. For example, using the association we can find features correlated to each other and thus emphasize removing anyone so as to remove some redundant features and improve processing power/time.
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 the goal. Though data mining is an evolving space, we have tried to create an exhaustive list for all types of tools in Data mining above for readers.
This is a guide to the Type of Data Mining. Here we discuss the Introduction and Top 12 Types of Data Mining. You can also go through our other suggested articles –