Data Mining Types
In this modern world, we need data mining where big data is surrounded, which can be predicted to grow by 40% by the next decade. We may think that the fact is we are starving for knowledge. But the main reason behind this data creates noise and also makes it difficult to mine. We can generate tons of data but still experience failing big data initiatives as the useful data is deeply buried inside. Therefore without any powerful tools like data mining, we cannot mine such data and we may not get any benefit.
Types of data mining
The following data mining techniques will serve many different business problems and also provide a different insight into each of them. Understanding the type of business problem we need to solve, will also help in knowing which technique will be the best to use, and will yield the best results. There are many types of Data mining.
- Predictive data mining analysis
In this Predictive data mining analysis, as the name signifies the predictive data mining analysis will work on the data that may help what will happen later in business. Predictive data mining will also be further classified as:
- Classification Analysis- This data mining type is generally used for fetching important relevant information about the data and metadata. It is also used to categorize the different types of data formats into different classes. In the classification analysis, we have to implement the algorithms to decide in which way the new data should be categorized. This technique is useful for retailers who will use it to study the buying habits of their different customers.
b) Regression Analysis- It is a process that is used to identify and analyze the relationship among the variables. It means one variable will be dependent on the other variable. It is used for prediction and forecasting purposes.
c) Time series Analysis- It is a sequence of data points that are usually recorded as a specific time interval of points. They are mostly on regular time intervals. Time series data mining will help in generating valuable information for long-term business.
d) Prediction Analysis- This is a technique mainly used to predict the relationship that exists between both independent and dependent variables as well as the independent variables alone. It can also be used to predict profit and sales which are dependent and independent variables.
- Descriptive Data mining Analysis-
The main important goal of this method is to summarize the given ‘data and relevant information. The main descriptive data mining task is divided into:
- Clustering Analysis- In this mining, the technique will be used to create meaningful object clusters that will contain similar characteristics. People get confused with the classification but they will not have any issues if they will not understand how both these techniques work.
b) Summarisation Analysis- The summarization analysis is used to store a group of data in a complex way and is also easier to understand.
c) Association rule learning- It can be considered a method that can help us to identify some interesting relations between different variables in large databases. This technique can also help to unpack the hidden patterns in the data which can identify the variables within the data.
d) Sequence Discovery Analysis- The primary aim is to discuss the interesting pattern in the data based on data subjective and objective measures of how interesting the task involves identifying the patterns that are in sequence and appear frequently concerning a frequency support measure. Many people may confuse it with time series and both the sequence discovery analysis and time series analysis contain the adjacent observations which are order dependent. If people can see both of them in a little more depth, their confusion will be easily avoided as the time series analysis technique contains numerical data whereas the sequence discovery analysis contains discrete values.
Questions
- Why do we need data mining?
- What is a predictive data mining type?
