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Difference Between Descriptive and Predictive Data Mining
Descriptive data mining and predictive data mining are mining techniques that are used to find useful information and patterns in large datasets. Descriptive data mining is a data mining technique that analyzes the past data to provide latest information on past events, while predictive data mining is a data mining technique that is used to analyze past data and provides answers of future queries.
Read this this article to learn more about descriptive and predictive data mining techniques and how they are different from each other.
What is Descriptive Data Mining?
Descriptive data mining is a data mining technique that identifies what happened in the past by analyzing the stored past data. The main objective of descriptive data mining is the summarization and transformation of data into useful information for monitoring and reporting purposes.
Since descriptive data mining analyzes stored data to answer past queries, hence it provides more accurate results. To answer these queries, it uses data aggregation and data mining. Descriptive data mining uses the reactive type approach to mine the data.
What is Predictive Data Mining?
Predictive data mining is another data mining technique which was developed to identify what can happen in future by analyzing the stored data. For this purpose, it uses the statics and forecasting methods.
Although predictive data mining answers the future queries, it does not ensure the accuracy of results. Its results are just predication based on past events.
Predicative data mining uses the proactive approach for data analysis. The practical examples of predicative data mining include predictive modelling, forecasting, simulation, etc.
Now, let us discuss the differences between descriptive data mining and predictive data mining in detail.
Difference between Descriptive and Predictive Data Mining
The following are the important differences between descriptive data mining and predictive data mining −
S.No. |
Descriptive Data Mining |
Predictive Data Mining |
|---|---|---|
1. |
It tries to understand what happened in the past by analyzing the stored data. |
It tries to understand what could happen in the future using past data analysis. |
2. |
The data it provides is accurate. |
It may not be accurate result. |
3. |
It provides standard reporting. It also provides ad-hoc reporting. |
It is used in predictive modelling, forecasting, simulation and alerts. |
4. |
It uses data aggregation and data mining. |
It uses statistics and forecasting methods. |
5. |
It uses a reactive approach. |
It uses a proactive approach. |
6. |
Answers questions such as −
|
Answers questions such as −
|
Conclusion
The most significant difference between the two is that descriptive data mining is used to know what happed in the past by using historical data, while predictive data mining is used to know what could happen in future by using this historical data.
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